Fix models/{model} URL (#6132)

* docs without usage

* no usage

* rm metric note

.github/workflows/release.yaml

@@ -28,9 +28,10 @@ jobs:
           security unlock-keychain -p password build.keychain
           security import certificate.p12 -k build.keychain -P $MACOS_SIGNING_KEY_PASSWORD -T /usr/bin/codesign
           security set-key-partition-list -S apple-tool:,apple:,codesign: -s -k password build.keychain
+          security set-keychain-settings -lut 3600 build.keychain
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - name: Build Darwin
         env:
@@ -86,7 +87,7 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - run: go get ./...
       - run: |
@@ -140,13 +141,13 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - name: 'Install ROCm'
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -217,7 +218,7 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - name: 'Install CUDA'
         run: |
@@ -305,7 +306,7 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - run: go get
       - uses: actions/download-artifact@v4
@@ -436,6 +437,7 @@ jobs:
     env:
       OLLAMA_SKIP_IMAGE_BUILD: '1'
       PUSH: '1'
+      GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
       - name: Set Version
@@ -459,15 +461,20 @@ jobs:
           ls -lh dist/
           (cd dist; sha256sum * > sha256sum.txt)
           cat dist/sha256sum.txt
-      - uses: ncipollo/release-action@v1
-        with:
-          name: ${{ env.RELEASE_VERSION }}
-          allowUpdates: true
-          artifacts: 'dist/*'
-          draft: true
-          prerelease: true
-          omitBodyDuringUpdate: true
-          generateReleaseNotes: true
-          omitDraftDuringUpdate: true
-          omitPrereleaseDuringUpdate: true
-          replacesArtifacts: true
+      - name: Create or update Release
+        run: |
+          echo "Looking for existing release for ${{ env.RELEASE_VERSION }}"
+          OLD_TAG=$(gh release ls --json name,tagName | jq -r ".[] | select(.name == \"${{ env.RELEASE_VERSION }}\") | .tagName")
+          if [ -n "$OLD_TAG" ]; then
+            echo "Updating release ${{ env.RELEASE_VERSION }} to point to new tag ${GITHUB_REF_NAME}"
+            gh release edit ${OLD_TAG} --tag ${GITHUB_REF_NAME}
+          else
+            echo "Creating new release ${{ env.RELEASE_VERSION }} pointing to tag ${GITHUB_REF_NAME}"
+            gh release create ${GITHUB_REF_NAME} \
+              --title ${{ env.RELEASE_VERSION }} \
+              --draft \
+              --generate-notes \
+              --prerelease
+          fi
+          echo "Uploading artifacts for tag ${GITHUB_REF_NAME}"
+          gh release upload ${GITHUB_REF_NAME} dist/* --clobber

.github/workflows/test.yaml

@@ -34,13 +34,13 @@ jobs:
             git diff-tree -r --no-commit-id --name-only \
               $(git merge-base ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }}) \
               ${{ github.event.pull_request.head.sha }} \
-              | xargs python3 -c "import sys; print(any([x.startswith('$1') for x in sys.argv[1:]]))"
+              | xargs python3 -c "import sys; from pathlib import Path; print(any(Path(x).match(glob) for x in sys.argv[1:] for glob in '$*'.split(' ')))"
           }
 
           {
-            echo GENERATE=$(changed llm/)
-            echo GENERATE_CUDA=$(changed llm/)
-            echo GENERATE_ROCM=$(changed llm/)
+            echo GENERATE=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
+            echo GENERATE_CUDA=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
+            echo GENERATE_ROCM=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
           } >>$GITHUB_OUTPUT
 
   generate:
@@ -58,11 +58,12 @@ jobs:
     runs-on: ${{ matrix.os }}
     env:
       GOARCH: ${{ matrix.arch }}
+      CGO_ENABLED: '1'
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - run: go get ./...
       - run: |
@@ -79,6 +80,7 @@ jobs:
       - run: go generate -x ./...
         if: ${{ ! startsWith(matrix.os, 'windows-') }}
         name: 'Unix Go Generate'
+      - run: go build .
       - uses: actions/upload-artifact@v4
         with:
           name: ${{ matrix.os }}-${{ matrix.arch }}-libraries
@@ -124,7 +126,7 @@ jobs:
     strategy:
       matrix:
         rocm-version:
-          - '6.0.2'
+          - '6.1.2'
     runs-on: linux
     container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
     steps:
@@ -161,13 +163,13 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - name: 'Install ROCm'
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -198,7 +200,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - name: 'Install CUDA'
         run: |
@@ -253,7 +255,7 @@ jobs:
           submodules: recursive
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: false
       - run: |
           case ${{ matrix.arch }} in
@@ -269,9 +271,9 @@ jobs:
           mkdir -p llm/build/darwin/$ARCH/stub/bin
           touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
         if: ${{ startsWith(matrix.os, 'macos-') }}
-      - uses: golangci/golangci-lint-action@v4
+      - uses: golangci/golangci-lint-action@v6
         with:
-          args: --timeout 8m0s -v
+          args: --timeout 8m0s -v ${{ startsWith(matrix.os, 'windows-') && '' || '--disable gofmt --disable goimports' }}
   test:
     strategy:
       matrix:
@@ -287,13 +289,15 @@ jobs:
       GOARCH: ${{ matrix.arch }}
       CGO_ENABLED: '1'
       OLLAMA_CPU_TARGET: 'static'
+      OLLAMA_SKIP_CPU_GENERATE: '1'
+      OLLAMA_SKIP_METAL_GENERATE: '1'
     steps:
       - uses: actions/checkout@v4
         with:
           submodules: recursive
       - uses: actions/setup-go@v5
         with:
-          go-version-file: go.mod
+          go-version: "stable"
           cache: true
       - run: |
           case ${{ matrix.arch }} in

.golangci.yaml

@@ -9,9 +9,26 @@ linters:
     - contextcheck
     - exportloopref
     - gocheckcompilerdirectives
-    # FIXME: for some reason this errors on windows
+    # conditionally enable this on linux/macos
     # - gofmt
     # - goimports
+    - intrange
     - misspell
     - nilerr
+    - nolintlint
+    - nosprintfhostport
+    - testifylint
+    - unconvert
     - unused
+    - wastedassign
+    - whitespace
+    - usestdlibvars
+severity:
+  default-severity: error
+  rules:
+    - linters:
+        - gofmt
+        - goimports
+        - intrange
+        - usestdlibvars
+      severity: info

Dockerfile

@@ -1,8 +1,8 @@
-ARG GOLANG_VERSION=1.22.1
+ARG GOLANG_VERSION=1.22.5
 ARG CMAKE_VERSION=3.22.1
 # this CUDA_VERSION corresponds with the one specified in docs/gpu.md
 ARG CUDA_VERSION=11.3.1
-ARG ROCM_VERSION=6.0.2
+ARG ROCM_VERSION=6.1.2
 
 # Copy the minimal context we need to run the generate scripts
 FROM scratch AS llm-code
@@ -70,12 +70,12 @@ RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx2-build-amd64
 RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
 
-FROM --platform=linux/arm64 centos:7 AS cpu-builder-arm64
+FROM --platform=linux/arm64 rockylinux:8 AS cpu-builder-arm64
 ARG CMAKE_VERSION
 ARG GOLANG_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
+ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
 COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 ARG OLLAMA_CUSTOM_CPU_DEFS
 ARG CGO_CFLAGS

README.md

@@ -6,7 +6,7 @@
 
 [![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
 
-Get up and running with large language models locally.
+Get up and running with large language models.
 
 ### macOS
 
@@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 ## Quickstart
 
-To run and chat with [Llama 3](https://ollama.com/library/llama3):
+To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1):
 
 ```
-ollama run llama3
+ollama run llama3.1
 ```
 
 ## Model library
@@ -49,20 +49,24 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                       |
 | ------------------ | ---------- | ----- | ------------------------------ |
-| Llama 3            | 8B         | 4.7GB | `ollama run llama3`            |
-| Llama 3            | 70B        | 40GB  | `ollama run llama3:70b`        |
-| Phi-3              | 3.8B       | 2.3GB | `ollama run phi3`              |
+| Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`          |
+| Llama 3.1          | 70B        | 40GB  | `ollama run llama3.1:70b`      |
+| Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`     |
+| Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`              |
+| Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`       |
+| Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`            |
+| Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`        |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
+| Moondream 2        | 1.4B       | 829MB | `ollama run moondream`         |
 | Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`       |
 | Starling           | 7B         | 4.1GB | `ollama run starling-lm`       |
 | Code Llama         | 7B         | 3.8GB | `ollama run codellama`         |
 | Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored` |
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`             |
-| Gemma              | 2B         | 1.4GB | `ollama run gemma:2b`          |
-| Gemma              | 7B         | 4.8GB | `ollama run gemma:7b`          |
 | Solar              | 10.7B      | 6.1GB | `ollama run solar`             |
 
-> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
+> [!NOTE]
+> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
 
 ## Customize a model
 
@@ -94,16 +98,16 @@ See the [guide](docs/import.md) on importing models for more information.
 
 ### Customize a prompt
 
-Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3` model:
+Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model:
 
 ```
-ollama pull llama3
+ollama pull llama3.1
 ```
 
 Create a `Modelfile`:
 
 ```
-FROM llama3
+FROM llama3.1
 
 # set the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -138,7 +142,7 @@ ollama create mymodel -f ./Modelfile
 ### Pull a model
 
 ```
-ollama pull llama3
+ollama pull llama3.1
 ```
 
 > This command can also be used to update a local model. Only the diff will be pulled.
@@ -146,13 +150,13 @@ ollama pull llama3
 ### Remove a model
 
 ```
-ollama rm llama3
+ollama rm llama3.1
 ```
 
 ### Copy a model
 
 ```
-ollama cp llama3 my-model
+ollama cp llama3.1 my-model
 ```
 
 ### Multiline input
@@ -169,17 +173,23 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
 ### Multimodal models
 
 ```
->>> What's in this image? /Users/jmorgan/Desktop/smile.png
+ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
 The image features a yellow smiley face, which is likely the central focus of the picture.
 ```
 
 ### Pass the prompt as an argument
 
 ```
-$ ollama run llama3 "Summarize this file: $(cat README.md)"
+$ ollama run llama3.1 "Summarize this file: $(cat README.md)"
  Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
 ```
 
+### Show model information
+
+```
+ollama show llama3.1
+```
+
 ### List models on your computer
 
 ```
@@ -192,25 +202,7 @@ ollama list
 
 ## Building
 
-Install `cmake` and `go`:
-
-```
-brew install cmake go
-```
-
-Then generate dependencies:
-
-```
-go generate ./...
-```
-
-Then build the binary:
-
-```
-go build .
-```
-
-More detailed instructions can be found in the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
+See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
 
 ### Running local builds
 
@@ -223,7 +215,7 @@ Next, start the server:
 Finally, in a separate shell, run a model:
 
 ```
-./ollama run llama3
+./ollama run llama3.1
 ```
 
 ## REST API
@@ -234,7 +226,7 @@ Ollama has a REST API for running and managing models.
 
 ```
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3",
+  "model": "llama3.1",
   "prompt":"Why is the sky blue?"
 }'
 ```
@@ -243,7 +235,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3",
+  "model": "llama3.1",
   "messages": [
     { "role": "user", "content": "why is the sky blue?" }
   ]
@@ -299,6 +291,15 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
 - [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
 - [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
+- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
+- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
+- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
+- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
+- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
+- [AI Studio](https://github.com/MindWorkAI/AI-Studio)
+- [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
+- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
+- [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
 
 ### Terminal
 
@@ -321,6 +322,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [ShellOracle](https://github.com/djcopley/ShellOracle)
 - [tlm](https://github.com/yusufcanb/tlm)
 - [podman-ollama](https://github.com/ericcurtin/podman-ollama)
+- [gollama](https://github.com/sammcj/gollama)
 
 ### Database
 
@@ -336,13 +338,16 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Libraries
 
 - [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa)
+- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
 - [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
+- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
 - [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html)
 - [LiteLLM](https://github.com/BerriAI/litellm)
 - [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
 - [Ollama for Ruby](https://github.com/gbaptista/ollama-ai)
 - [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
+- [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp)
 - [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
 - [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
 - [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
@@ -359,7 +364,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Testcontainers](https://testcontainers.com/modules/ollama/)
 - [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
 - [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
-- [LlamaScript](https://github.com/WolfTheDeveloper/llamascript)
+- [LlamaScript](https://github.com/Project-Llama/llamascript)
+
 ### Mobile
 
 - [Enchanted](https://github.com/AugustDev/enchanted)
@@ -391,7 +397,10 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
 - [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
 - [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
+- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
+- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
 
 ### Supported backends
+
 - [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
 

SECURITY.md

@@ -0,0 +1,25 @@
+# Security
+
+The Ollama maintainer team takes security seriously and will actively work to resolve security issues.
+
+## Reporting a vulnerability
+
+If you discover a security vulnerability, please do not open a public issue. Instead, please report it by emailing hello@ollama.com. We ask that you give us sufficient time to investigate and address the vulnerability before disclosing it publicly.
+
+Please include the following details in your report:
+- A description of the vulnerability
+- Steps to reproduce the issue
+- Your assessment of the potential impact
+- Any possible mitigations
+
+## Security best practices
+
+While the maintainer team does their best to secure Ollama, users are encouraged to implement their own security best practices, such as:
+
+- Regularly updating to the latest version of Ollama
+- Securing access to hosted instances of Ollama
+- Monitoring systems for unusual activity
+
+## Contact
+
+For any other questions or concerns related to security, please contact us at hello@ollama.com

api/client.go

@@ -20,14 +20,11 @@ import (
 	"encoding/json"
 	"fmt"
 	"io"
-	"net"
 	"net/http"
 	"net/url"
-	"os"
 	"runtime"
-	"strconv"
-	"strings"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/version"
 )
@@ -65,66 +62,12 @@ func checkError(resp *http.Response, body []byte) error {
 // If the variable is not specified, a default ollama host and port will be
 // used.
 func ClientFromEnvironment() (*Client, error) {
-	ollamaHost, err := GetOllamaHost()
-	if err != nil {
-		return nil, err
-	}
-
 	return &Client{
-		base: &url.URL{
-			Scheme: ollamaHost.Scheme,
-			Host:   net.JoinHostPort(ollamaHost.Host, ollamaHost.Port),
-		},
+		base: envconfig.Host(),
 		http: http.DefaultClient,
 	}, nil
 }
 
-type OllamaHost struct {
-	Scheme string
-	Host   string
-	Port   string
-}
-
-func GetOllamaHost() (OllamaHost, error) {
-	defaultPort := "11434"
-
-	hostVar := os.Getenv("OLLAMA_HOST")
-	hostVar = strings.TrimSpace(strings.Trim(strings.TrimSpace(hostVar), "\"'"))
-
-	scheme, hostport, ok := strings.Cut(hostVar, "://")
-	switch {
-	case !ok:
-		scheme, hostport = "http", hostVar
-	case scheme == "http":
-		defaultPort = "80"
-	case scheme == "https":
-		defaultPort = "443"
-	}
-
-	// trim trailing slashes
-	hostport = strings.TrimRight(hostport, "/")
-
-	host, port, err := net.SplitHostPort(hostport)
-	if err != nil {
-		host, port = "127.0.0.1", defaultPort
-		if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
-			host = ip.String()
-		} else if hostport != "" {
-			host = hostport
-		}
-	}
-
-	if portNum, err := strconv.ParseInt(port, 10, 32); err != nil || portNum > 65535 || portNum < 0 {
-		return OllamaHost{}, ErrInvalidHostPort
-	}
-
-	return OllamaHost{
-		Scheme: scheme,
-		Host:   host,
-		Port:   port,
-	}, nil
-}
-
 func NewClient(base *url.URL, http *http.Client) *Client {
 	return &Client{
 		base: base,
@@ -354,6 +297,15 @@ func (c *Client) List(ctx context.Context) (*ListResponse, error) {
 	return &lr, nil
 }
 
+// List running models.
+func (c *Client) ListRunning(ctx context.Context) (*ProcessResponse, error) {
+	var lr ProcessResponse
+	if err := c.do(ctx, http.MethodGet, "/api/ps", nil, &lr); err != nil {
+		return nil, err
+	}
+	return &lr, nil
+}
+
 // Copy copies a model - creating a model with another name from an existing
 // model.
 func (c *Client) Copy(ctx context.Context, req *CopyRequest) error {
@@ -389,7 +341,16 @@ func (c *Client) Heartbeat(ctx context.Context) error {
 	return nil
 }
 
-// Embeddings generates embeddings from a model.
+// Embed generates embeddings from a model.
+func (c *Client) Embed(ctx context.Context, req *EmbedRequest) (*EmbedResponse, error) {
+	var resp EmbedResponse
+	if err := c.do(ctx, http.MethodPost, "/api/embed", req, &resp); err != nil {
+		return nil, err
+	}
+	return &resp, nil
+}
+
+// Embeddings generates an embedding from a model.
 func (c *Client) Embeddings(ctx context.Context, req *EmbeddingRequest) (*EmbeddingResponse, error) {
 	var resp EmbeddingResponse
 	if err := c.do(ctx, http.MethodPost, "/api/embeddings", req, &resp); err != nil {

api/client_test.go

@@ -1,11 +1,7 @@
 package api
 
 import (
-	"fmt"
-	"net"
 	"testing"
-
-	"github.com/stretchr/testify/assert"
 )
 
 func TestClientFromEnvironment(t *testing.T) {
@@ -46,40 +42,4 @@ func TestClientFromEnvironment(t *testing.T) {
 			}
 		})
 	}
-
-	hostTestCases := map[string]*testCase{
-		"empty":               {value: "", expect: "127.0.0.1:11434"},
-		"only address":        {value: "1.2.3.4", expect: "1.2.3.4:11434"},
-		"only port":           {value: ":1234", expect: ":1234"},
-		"address and port":    {value: "1.2.3.4:1234", expect: "1.2.3.4:1234"},
-		"hostname":            {value: "example.com", expect: "example.com:11434"},
-		"hostname and port":   {value: "example.com:1234", expect: "example.com:1234"},
-		"zero port":           {value: ":0", expect: ":0"},
-		"too large port":      {value: ":66000", err: ErrInvalidHostPort},
-		"too small port":      {value: ":-1", err: ErrInvalidHostPort},
-		"ipv6 localhost":      {value: "[::1]", expect: "[::1]:11434"},
-		"ipv6 world open":     {value: "[::]", expect: "[::]:11434"},
-		"ipv6 no brackets":    {value: "::1", expect: "[::1]:11434"},
-		"ipv6 + port":         {value: "[::1]:1337", expect: "[::1]:1337"},
-		"extra space":         {value: " 1.2.3.4 ", expect: "1.2.3.4:11434"},
-		"extra quotes":        {value: "\"1.2.3.4\"", expect: "1.2.3.4:11434"},
-		"extra space+quotes":  {value: " \" 1.2.3.4 \" ", expect: "1.2.3.4:11434"},
-		"extra single quotes": {value: "'1.2.3.4'", expect: "1.2.3.4:11434"},
-	}
-
-	for k, v := range hostTestCases {
-		t.Run(k, func(t *testing.T) {
-			t.Setenv("OLLAMA_HOST", v.value)
-
-			oh, err := GetOllamaHost()
-			if err != v.err {
-				t.Fatalf("expected %s, got %s", v.err, err)
-			}
-
-			if err == nil {
-				host := net.JoinHostPort(oh.Host, oh.Port)
-				assert.Equal(t, v.expect, host, fmt.Sprintf("%s: expected %s, got %s", k, v.expect, host))
-			}
-		})
-	}
 }

api/types.go

@@ -2,7 +2,6 @@ package api
 
 import (
 	"encoding/json"
-	"errors"
 	"fmt"
 	"log/slog"
 	"math"
@@ -48,6 +47,9 @@ type GenerateRequest struct {
 	// Prompt is the textual prompt to send to the model.
 	Prompt string `json:"prompt"`
 
+	// Suffix is the text that comes after the inserted text.
+	Suffix string `json:"suffix"`
+
 	// System overrides the model's default system message/prompt.
 	System string `json:"system"`
 
@@ -98,10 +100,25 @@ type ChatRequest struct {
 	// followin the request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
+	// Tools is an optional list of tools the model has access to.
+	Tools `json:"tools,omitempty"`
+
 	// Options lists model-specific options.
 	Options map[string]interface{} `json:"options"`
 }
 
+type Tools []Tool
+
+func (t Tools) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
+func (t Tool) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
 // Message is a single message in a chat sequence. The message contains the
 // role ("system", "user", or "assistant"), the content and an optional list
 // of images.
@@ -109,6 +126,59 @@ type Message struct {
 	Role      string      `json:"role"`
 	Content   string      `json:"content"`
 	Images    []ImageData `json:"images,omitempty"`
+	ToolCalls []ToolCall  `json:"tool_calls,omitempty"`
+}
+
+func (m *Message) UnmarshalJSON(b []byte) error {
+	type Alias Message
+	var a Alias
+	if err := json.Unmarshal(b, &a); err != nil {
+		return err
+	}
+
+	*m = Message(a)
+	m.Role = strings.ToLower(m.Role)
+	return nil
+}
+
+type ToolCall struct {
+	Function ToolCallFunction `json:"function"`
+}
+
+type ToolCallFunction struct {
+	Name      string                    `json:"name"`
+	Arguments ToolCallFunctionArguments `json:"arguments"`
+}
+
+type ToolCallFunctionArguments map[string]any
+
+func (t *ToolCallFunctionArguments) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
+type Tool struct {
+	Type     string       `json:"type"`
+	Function ToolFunction `json:"function"`
+}
+
+type ToolFunction struct {
+	Name        string `json:"name"`
+	Description string `json:"description"`
+	Parameters  struct {
+		Type       string   `json:"type"`
+		Required   []string `json:"required"`
+		Properties map[string]struct {
+			Type        string   `json:"type"`
+			Description string   `json:"description"`
+			Enum        []string `json:"enum,omitempty"`
+		} `json:"properties"`
+	} `json:"parameters"`
+}
+
+func (t *ToolFunction) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
 }
 
 // ChatResponse is the response returned by [Client.Chat]. Its fields are
@@ -144,6 +214,7 @@ type Options struct {
 	NumPredict       int      `json:"num_predict,omitempty"`
 	TopK             int      `json:"top_k,omitempty"`
 	TopP             float32  `json:"top_p,omitempty"`
+	MinP             float32  `json:"min_p,omitempty"`
 	TFSZ             float32  `json:"tfs_z,omitempty"`
 	TypicalP         float32  `json:"typical_p,omitempty"`
 	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
@@ -169,11 +240,39 @@ type Runner struct {
 	F16KV     bool  `json:"f16_kv,omitempty"`
 	LogitsAll bool  `json:"logits_all,omitempty"`
 	VocabOnly bool  `json:"vocab_only,omitempty"`
-	UseMMap   bool `json:"use_mmap,omitempty"`
+	UseMMap   *bool `json:"use_mmap,omitempty"`
 	UseMLock  bool  `json:"use_mlock,omitempty"`
 	NumThread int   `json:"num_thread,omitempty"`
 }
 
+// EmbedRequest is the request passed to [Client.Embed].
+type EmbedRequest struct {
+	// Model is the model name.
+	Model string `json:"model"`
+
+	// Input is the input to embed.
+	Input any `json:"input"`
+
+	// KeepAlive controls how long the model will stay loaded in memory following
+	// this request.
+	KeepAlive *Duration `json:"keep_alive,omitempty"`
+
+	Truncate *bool `json:"truncate,omitempty"`
+
+	// Options lists model-specific options.
+	Options map[string]interface{} `json:"options"`
+}
+
+// EmbedResponse is the response from [Client.Embed].
+type EmbedResponse struct {
+	Model      string      `json:"model"`
+	Embeddings [][]float32 `json:"embeddings"`
+
+	TotalDuration   time.Duration `json:"total_duration,omitempty"`
+	LoadDuration    time.Duration `json:"load_duration,omitempty"`
+	PromptEvalCount int           `json:"prompt_eval_count,omitempty"`
+}
+
 // EmbeddingRequest is the request passed to [Client.Embeddings].
 type EmbeddingRequest struct {
 	// Model is the model name.
@@ -222,7 +321,10 @@ type DeleteRequest struct {
 type ShowRequest struct {
 	Model  string `json:"model"`
 	System string `json:"system"`
+
+	// Template is deprecated
 	Template string `json:"template"`
+	Verbose  bool   `json:"verbose"`
 
 	Options map[string]interface{} `json:"options"`
 
@@ -239,6 +341,9 @@ type ShowResponse struct {
 	System        string         `json:"system,omitempty"`
 	Details       ModelDetails   `json:"details,omitempty"`
 	Messages      []Message      `json:"messages,omitempty"`
+	ModelInfo     map[string]any `json:"model_info,omitempty"`
+	ProjectorInfo map[string]any `json:"projector_info,omitempty"`
+	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
 }
 
 // CopyRequest is the request passed to [Client.Copy].
@@ -282,11 +387,16 @@ type PushRequest struct {
 
 // ListResponse is the response from [Client.List].
 type ListResponse struct {
-	Models []ModelResponse `json:"models"`
+	Models []ListModelResponse `json:"models"`
 }
 
-// ModelResponse is a single model description in [ListResponse].
-type ModelResponse struct {
+// ProcessResponse is the response from [Client.Process].
+type ProcessResponse struct {
+	Models []ProcessModelResponse `json:"models"`
+}
+
+// ListModelResponse is a single model description in [ListResponse].
+type ListModelResponse struct {
 	Name       string       `json:"name"`
 	Model      string       `json:"model"`
 	ModifiedAt time.Time    `json:"modified_at"`
@@ -295,6 +405,24 @@ type ModelResponse struct {
 	Details    ModelDetails `json:"details,omitempty"`
 }
 
+// ProcessModelResponse is a single model description in [ProcessResponse].
+type ProcessModelResponse struct {
+	Name      string       `json:"name"`
+	Model     string       `json:"model"`
+	Size      int64        `json:"size"`
+	Digest    string       `json:"digest"`
+	Details   ModelDetails `json:"details,omitempty"`
+	ExpiresAt time.Time    `json:"expires_at"`
+	SizeVRAM  int64        `json:"size_vram"`
+}
+
+type RetrieveModelResponse struct {
+	Id      string `json:"id"`
+	Object  string `json:"object"`
+	Created int64  `json:"created"`
+	OwnedBy string `json:"owned_by"`
+}
+
 type TokenResponse struct {
 	Token string `json:"token"`
 }
@@ -361,8 +489,6 @@ func (m *Metrics) Summary() {
 	}
 }
 
-var ErrInvalidHostPort = errors.New("invalid port specified in OLLAMA_HOST")
-
 func (opts *Options) FromMap(m map[string]interface{}) error {
 	valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
 	typeOpts := reflect.TypeOf(opts).Elem()   // types of the fields in the options struct
@@ -435,6 +561,17 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 					slice[i] = str
 				}
 				field.Set(reflect.ValueOf(slice))
+			case reflect.Pointer:
+				var b bool
+				if field.Type() == reflect.TypeOf(&b) {
+					val, ok := val.(bool)
+					if !ok {
+						return fmt.Errorf("option %q must be of type boolean", key)
+					}
+					field.Set(reflect.ValueOf(&val))
+				} else {
+					return fmt.Errorf("unknown type loading config params: %v %v", field.Kind(), field.Type())
+				}
 			default:
 				return fmt.Errorf("unknown type loading config params: %v", field.Kind())
 			}
@@ -477,7 +614,7 @@ func DefaultOptions() Options {
 			LowVRAM:   false,
 			F16KV:     true,
 			UseMLock:  false,
-			UseMMap:   true,
+			UseMMap:   nil,
 			UseNUMA:   false,
 		},
 	}
@@ -574,6 +711,17 @@ func FormatParams(params map[string][]string) (map[string]interface{}, error) {
 				case reflect.Slice:
 					// TODO: only string slices are supported right now
 					out[key] = vals
+				case reflect.Pointer:
+					var b bool
+					if field.Type() == reflect.TypeOf(&b) {
+						boolVal, err := strconv.ParseBool(vals[0])
+						if err != nil {
+							return nil, fmt.Errorf("invalid bool value %s", vals)
+						}
+						out[key] = &boolVal
+					} else {
+						return nil, fmt.Errorf("unknown type %s for %s", field.Kind(), key)
+					}
 				default:
 					return nil, fmt.Errorf("unknown type %s for %s", field.Kind(), key)
 				}

api/types_test.go

@@ -2,6 +2,7 @@ package api
 
 import (
 	"encoding/json"
+	"fmt"
 	"math"
 	"testing"
 	"time"
@@ -72,13 +73,13 @@ func TestDurationMarshalUnmarshal(t *testing.T) {
 		},
 		{
 			"positive duration",
-			time.Duration(42 * time.Second),
-			time.Duration(42 * time.Second),
+			42 * time.Second,
+			42 * time.Second,
 		},
 		{
 			"another positive duration",
-			time.Duration(42 * time.Minute),
-			time.Duration(42 * time.Minute),
+			42 * time.Minute,
+			42 * time.Minute,
 		},
 		{
 			"zero duration",
@@ -105,3 +106,128 @@ func TestDurationMarshalUnmarshal(t *testing.T) {
 		})
 	}
 }
+
+func TestUseMmapParsingFromJSON(t *testing.T) {
+	tr := true
+	fa := false
+	tests := []struct {
+		name string
+		req  string
+		exp  *bool
+	}{
+		{
+			name: "Undefined",
+			req:  `{ }`,
+			exp:  nil,
+		},
+		{
+			name: "True",
+			req:  `{ "use_mmap": true }`,
+			exp:  &tr,
+		},
+		{
+			name: "False",
+			req:  `{ "use_mmap": false }`,
+			exp:  &fa,
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			var oMap map[string]interface{}
+			err := json.Unmarshal([]byte(test.req), &oMap)
+			require.NoError(t, err)
+			opts := DefaultOptions()
+			err = opts.FromMap(oMap)
+			require.NoError(t, err)
+			assert.Equal(t, test.exp, opts.UseMMap)
+		})
+	}
+}
+
+func TestUseMmapFormatParams(t *testing.T) {
+	tr := true
+	fa := false
+	tests := []struct {
+		name string
+		req  map[string][]string
+		exp  *bool
+		err  error
+	}{
+		{
+			name: "True",
+			req: map[string][]string{
+				"use_mmap": {"true"},
+			},
+			exp: &tr,
+			err: nil,
+		},
+		{
+			name: "False",
+			req: map[string][]string{
+				"use_mmap": {"false"},
+			},
+			exp: &fa,
+			err: nil,
+		},
+		{
+			name: "Numeric True",
+			req: map[string][]string{
+				"use_mmap": {"1"},
+			},
+			exp: &tr,
+			err: nil,
+		},
+		{
+			name: "Numeric False",
+			req: map[string][]string{
+				"use_mmap": {"0"},
+			},
+			exp: &fa,
+			err: nil,
+		},
+		{
+			name: "invalid string",
+			req: map[string][]string{
+				"use_mmap": {"foo"},
+			},
+			exp: nil,
+			err: fmt.Errorf("invalid bool value [foo]"),
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			resp, err := FormatParams(test.req)
+			require.Equal(t, test.err, err)
+			respVal, ok := resp["use_mmap"]
+			if test.exp != nil {
+				assert.True(t, ok, "resp: %v", resp)
+				assert.Equal(t, *test.exp, *respVal.(*bool))
+			}
+		})
+	}
+}
+
+func TestMessage_UnmarshalJSON(t *testing.T) {
+	tests := []struct {
+		input    string
+		expected string
+	}{
+		{`{"role": "USER", "content": "Hello!"}`, "user"},
+		{`{"role": "System", "content": "Initialization complete."}`, "system"},
+		{`{"role": "assistant", "content": "How can I help you?"}`, "assistant"},
+		{`{"role": "TOOl", "content": "Access granted."}`, "tool"},
+	}
+
+	for _, test := range tests {
+		var msg Message
+		if err := json.Unmarshal([]byte(test.input), &msg); err != nil {
+			t.Errorf("Unexpected error: %v", err)
+		}
+
+		if msg.Role != test.expected {
+			t.Errorf("role not lowercased: got %v, expected %v", msg.Role, test.expected)
+		}
+	}
+}

app/lifecycle/logging.go

@@ -5,14 +5,16 @@ import (
 	"log/slog"
 	"os"
 	"path/filepath"
+	"strconv"
+	"strings"
 
-	"github.com/ollama/ollama/server/envconfig"
+	"github.com/ollama/ollama/envconfig"
 )
 
 func InitLogging() {
 	level := slog.LevelInfo
 
-	if envconfig.Debug {
+	if envconfig.Debug() {
 		level = slog.LevelDebug
 	}
 
@@ -24,6 +26,7 @@ func InitLogging() {
 		logFile = os.Stderr
 		// TODO - write one-line to the app.log file saying we're running in console mode to help avoid confusion
 	} else {
+		rotateLogs(AppLogFile)
 		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 		if err != nil {
 			slog.Error(fmt.Sprintf("failed to create server log %v", err))
@@ -46,3 +49,32 @@ func InitLogging() {
 
 	slog.Info("ollama app started")
 }
+
+func rotateLogs(logFile string) {
+	if _, err := os.Stat(logFile); os.IsNotExist(err) {
+		return
+	}
+	index := strings.LastIndex(logFile, ".")
+	pre := logFile[:index]
+	post := "." + logFile[index+1:]
+	for i := LogRotationCount; i > 0; i-- {
+		older := pre + "-" + strconv.Itoa(i) + post
+		newer := pre + "-" + strconv.Itoa(i-1) + post
+		if i == 1 {
+			newer = pre + post
+		}
+		if _, err := os.Stat(newer); err == nil {
+			if _, err := os.Stat(older); err == nil {
+				err := os.Remove(older)
+				if err != nil {
+					slog.Warn("Failed to remove older log", "older", older, "error", err)
+					continue
+				}
+			}
+			err := os.Rename(newer, older)
+			if err != nil {
+				slog.Warn("Failed to rotate log", "older", older, "newer", newer, "error", err)
+			}
+		}
+	}
+}

app/lifecycle/logging_test.go

@@ -0,0 +1,44 @@
+package lifecycle
+
+import (
+	"os"
+	"path/filepath"
+	"strconv"
+	"testing"
+
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
+)
+
+func TestRotateLogs(t *testing.T) {
+	logDir := t.TempDir()
+	logFile := filepath.Join(logDir, "testlog.log")
+
+	// No log exists
+	rotateLogs(logFile)
+
+	require.NoError(t, os.WriteFile(logFile, []byte("1"), 0644))
+	assert.FileExists(t, logFile)
+	// First rotation
+	rotateLogs(logFile)
+	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
+	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
+	assert.NoFileExists(t, logFile)
+
+	// Should be a no-op without a new log
+	rotateLogs(logFile)
+	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
+	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
+	assert.NoFileExists(t, logFile)
+
+	for i := 2; i <= LogRotationCount+1; i++ {
+		require.NoError(t, os.WriteFile(logFile, []byte(strconv.Itoa(i)), 0644))
+		assert.FileExists(t, logFile)
+		rotateLogs(logFile)
+		assert.NoFileExists(t, logFile)
+		for j := 1; j < i; j++ {
+			assert.FileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(j)+".log"))
+		}
+		assert.NoFileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(i+1)+".log"))
+	}
+}

app/lifecycle/paths.go

@@ -21,6 +21,7 @@ var (
 	ServerLogFile    = "/tmp/ollama.log"
 	UpgradeLogFile   = "/tmp/ollama_update.log"
 	Installer        = "OllamaSetup.exe"
+	LogRotationCount = 5
 )
 
 func init() {
@@ -69,7 +70,6 @@ func init() {
 				slog.Error(fmt.Sprintf("create ollama dir %s: %v", AppDataDir, err))
 			}
 		}
-
 	} else if runtime.GOOS == "darwin" {
 		// TODO
 		AppName += ".app"

app/lifecycle/server.go

@@ -15,7 +15,7 @@ import (
 )
 
 func getCLIFullPath(command string) string {
-	cmdPath := ""
+	var cmdPath string
 	appExe, err := os.Executable()
 	if err == nil {
 		cmdPath = filepath.Join(filepath.Dir(appExe), command)
@@ -54,7 +54,7 @@ func start(ctx context.Context, command string) (*exec.Cmd, error) {
 		return nil, fmt.Errorf("failed to spawn server stderr pipe: %w", err)
 	}
 
-	// TODO - rotation
+	rotateLogs(ServerLogFile)
 	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 	if err != nil {
 		return nil, fmt.Errorf("failed to create server log: %w", err)
@@ -65,7 +65,6 @@ func start(ctx context.Context, command string) (*exec.Cmd, error) {
 	if err != nil {
 		if !errors.Is(err, os.ErrNotExist) {
 			return nil, fmt.Errorf("stat ollama server log dir %s: %v", logDir, err)
-
 		}
 
 		if err := os.MkdirAll(logDir, 0o755); err != nil {

app/lifecycle/server_windows.go

@@ -24,7 +24,8 @@ func terminate(cmd *exec.Cmd) error {
 	if err != nil {
 		return err
 	}
-	defer dll.Release() // nolint: errcheck
+	//nolint:errcheck
+	defer dll.Release()
 
 	pid := cmd.Process.Pid
 
@@ -73,7 +74,8 @@ func isProcessExited(pid int) (bool, error) {
 	if err != nil {
 		return false, fmt.Errorf("failed to open process: %v", err)
 	}
-	defer windows.CloseHandle(hProcess) // nolint: errcheck
+	//nolint:errcheck
+	defer windows.CloseHandle(hProcess)
 
 	var exitCode uint32
 	err = windows.GetExitCodeProcess(hProcess, &exitCode)

app/lifecycle/updater.go

@@ -78,7 +78,7 @@ func IsNewReleaseAvailable(ctx context.Context) (bool, UpdateResponse) {
 	}
 	defer resp.Body.Close()
 
-	if resp.StatusCode == 204 {
+	if resp.StatusCode == http.StatusNoContent {
 		slog.Debug("check update response 204 (current version is up to date)")
 		return false, updateResp
 	}
@@ -87,7 +87,7 @@ func IsNewReleaseAvailable(ctx context.Context) (bool, UpdateResponse) {
 		slog.Warn(fmt.Sprintf("failed to read body response: %s", err))
 	}
 
-	if resp.StatusCode != 200 {
+	if resp.StatusCode != http.StatusOK {
 		slog.Info(fmt.Sprintf("check update error %d - %.96s", resp.StatusCode, string(body)))
 		return false, updateResp
 	}
@@ -114,7 +114,7 @@ func DownloadNewRelease(ctx context.Context, updateResp UpdateResponse) error {
 	if err != nil {
 		return fmt.Errorf("error checking update: %w", err)
 	}
-	if resp.StatusCode != 200 {
+	if resp.StatusCode != http.StatusOK {
 		return fmt.Errorf("unexpected status attempting to download update %d", resp.StatusCode)
 	}
 	resp.Body.Close()

app/ollama.iss

@@ -88,10 +88,15 @@ DialogFontSize=12
 [Files]
 Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
 Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
-Source: "..\dist\windows-{#ARCH}\*.dll"; DestDir: "{app}"; Flags: ignoreversion 64bit
 Source: "..\dist\windows-{#ARCH}\ollama_runners\*"; DestDir: "{app}\ollama_runners"; Flags: ignoreversion 64bit recursesubdirs
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
 Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
+#if DirExists("..\dist\windows-amd64\cuda")
+  Source: "..\dist\windows-amd64\cuda\*"; DestDir: "{app}\cuda\"; Flags: ignoreversion recursesubdirs
+#endif
+#if DirExists("..\dist\windows-amd64\oneapi")
+  Source: "..\dist\windows-amd64\oneapi\*"; DestDir: "{app}\oneapi\"; Flags: ignoreversion recursesubdirs
+#endif
 #if DirExists("..\dist\windows-amd64\rocm")
   Source: "..\dist\windows-amd64\rocm\*"; DestDir: "{app}\rocm\"; Flags: ignoreversion recursesubdirs
 #endif
@@ -122,6 +127,10 @@ Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\models"
 Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\history"
 ; NOTE: if the user has a custom OLLAMA_MODELS it will be preserved
 
+[InstallDelete]
+Type: filesandordirs; Name: "{%TEMP}\ollama*"
+Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
+
 [Messages]
 WizardReady=Ollama Windows Preview
 ReadyLabel1=%nLet's get you up and running with your own large language models.
@@ -129,7 +138,7 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi
 
 
 ;FinishedHeadingLabel=Run your first model
-;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3
+;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3.1
 ;ClickFinish=%n
 
 [Registry]

app/ollama_welcome.ps1

@@ -4,5 +4,5 @@ write-host "Welcome to Ollama!"
 write-host ""
 write-host "Run your first model:"
 write-host ""
-write-host "`tollama run llama2"
+write-host "`tollama run llama3.1"
 write-host ""

app/store/store.go

@@ -29,7 +29,6 @@ func GetID() string {
 		initStore()
 	}
 	return store.ID
-
 }
 
 func GetFirstTimeRun() bool {

app/tray/wintray/eventloop.go

@@ -47,7 +47,6 @@ func nativeLoop() {
 		default:
 			pTranslateMessage.Call(uintptr(unsafe.Pointer(m))) //nolint:errcheck
 			pDispatchMessage.Call(uintptr(unsafe.Pointer(m)))  //nolint:errcheck
-
 		}
 	}
 }
@@ -160,8 +159,8 @@ func (t *winTray) wndProc(hWnd windows.Handle, message uint32, wParam, lParam ui
 		lResult, _, _ = pDefWindowProc.Call(
 			uintptr(hWnd),
 			uintptr(message),
-			uintptr(wParam),
-			uintptr(lParam),
+			wParam,
+			lParam,
 		)
 	}
 	return

app/tray/wintray/tray.go

@@ -186,7 +186,7 @@ func (t *winTray) initInstance() error {
 	t.muNID.Lock()
 	defer t.muNID.Unlock()
 	t.nid = &notifyIconData{
-		Wnd:             windows.Handle(t.window),
+		Wnd:             t.window,
 		ID:              100,
 		Flags:           NIF_MESSAGE,
 		CallbackMessage: t.wmSystrayMessage,
@@ -197,7 +197,6 @@ func (t *winTray) initInstance() error {
 }
 
 func (t *winTray) createMenu() error {
-
 	menuHandle, _, err := pCreatePopupMenu.Call()
 	if menuHandle == 0 {
 		return err
@@ -246,7 +245,7 @@ func (t *winTray) addOrUpdateMenuItem(menuItemId uint32, parentId uint32, title
 	mi := menuItemInfo{
 		Mask:     MIIM_FTYPE | MIIM_STRING | MIIM_ID | MIIM_STATE,
 		Type:     MFT_STRING,
-		ID:       uint32(menuItemId),
+		ID:       menuItemId,
 		TypeData: titlePtr,
 		Cch:      uint32(len(title)),
 	}
@@ -302,11 +301,10 @@ func (t *winTray) addOrUpdateMenuItem(menuItemId uint32, parentId uint32, title
 }
 
 func (t *winTray) addSeparatorMenuItem(menuItemId, parentId uint32) error {
-
 	mi := menuItemInfo{
 		Mask: MIIM_FTYPE | MIIM_ID | MIIM_STATE,
 		Type: MFT_SEPARATOR,
-		ID:   uint32(menuItemId),
+		ID:   menuItemId,
 	}
 
 	mi.Size = uint32(unsafe.Sizeof(mi))
@@ -426,7 +424,6 @@ func iconBytesToFilePath(iconBytes []byte) (string, error) {
 // Loads an image from file and shows it in tray.
 // Shell_NotifyIcon: https://msdn.microsoft.com/en-us/library/windows/desktop/bb762159(v=vs.85).aspx
 func (t *winTray) setIcon(src string) error {
-
 	h, err := t.loadIconFrom(src)
 	if err != nil {
 		return err
@@ -444,7 +441,6 @@ func (t *winTray) setIcon(src string) error {
 // Loads an image from file to be shown in tray or menu item.
 // LoadImage: https://msdn.microsoft.com/en-us/library/windows/desktop/ms648045(v=vs.85).aspx
 func (t *winTray) loadIconFrom(src string) (windows.Handle, error) {
-
 	// Save and reuse handles of loaded images
 	t.muLoadedImages.RLock()
 	h, ok := t.loadedImages[src]

cmd/cmd.go

@@ -12,6 +12,7 @@ import (
 	"fmt"
 	"io"
 	"log"
+	"math"
 	"net"
 	"net/http"
 	"os"
@@ -19,21 +20,23 @@ import (
 	"path/filepath"
 	"regexp"
 	"runtime"
+	"slices"
 	"strings"
 	"syscall"
 	"time"
 
 	"github.com/containerd/console"
-
+	"github.com/mattn/go-runewidth"
 	"github.com/olekukonko/tablewriter"
 	"github.com/spf13/cobra"
 	"golang.org/x/crypto/ssh"
-	"golang.org/x/exp/slices"
 	"golang.org/x/term"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/auth"
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/server"
 	"github.com/ollama/ollama/types/errtypes"
@@ -62,7 +65,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	}
 	defer f.Close()
 
-	modelfile, err := model.ParseFile(f)
+	modelfile, err := parser.ParseFile(f)
 	if err != nil {
 		return err
 	}
@@ -159,9 +162,6 @@ func tempZipFiles(path string) (string, error) {
 	}
 	defer tempfile.Close()
 
-	zipfile := zip.NewWriter(tempfile)
-	defer zipfile.Close()
-
 	detectContentType := func(path string) (string, error) {
 		f, err := os.Open(path)
 		if err != nil {
@@ -206,7 +206,7 @@ func tempZipFiles(path string) (string, error) {
 		// pytorch files might also be unresolved git lfs references; skip if they are
 		// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
 		files = append(files, pt...)
-	} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/octet-stream"); len(pt) > 0 {
+	} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/zip"); len(pt) > 0 {
 		// pytorch files might also be unresolved git lfs references; skip if they are
 		// covers consolidated.x.pth, consolidated.pth
 		files = append(files, pt...)
@@ -230,6 +230,9 @@ func tempZipFiles(path string) (string, error) {
 		files = append(files, tks...)
 	}
 
+	zipfile := zip.NewWriter(tempfile)
+	defer zipfile.Close()
+
 	for _, file := range files {
 		f, err := os.Open(file)
 		if err != nil {
@@ -284,38 +287,12 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string) (string, er
 }
 
 func RunHandler(cmd *cobra.Command, args []string) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
-	name := args[0]
-
-	// check if the model exists on the server
-	show, err := client.Show(cmd.Context(), &api.ShowRequest{Name: name})
-	var statusError api.StatusError
-	switch {
-	case errors.As(err, &statusError) && statusError.StatusCode == http.StatusNotFound:
-		if err := PullHandler(cmd, []string{name}); err != nil {
-			return err
-		}
-
-		show, err = client.Show(cmd.Context(), &api.ShowRequest{Name: name})
-		if err != nil {
-			return err
-		}
-	case err != nil:
-		return err
-	}
-
 	interactive := true
 
 	opts := runOptions{
 		Model:    args[0],
 		WordWrap: os.Getenv("TERM") == "xterm-256color",
 		Options:  map[string]interface{}{},
-		MultiModal:  slices.Contains(show.Details.Families, "clip"),
-		ParentModel: show.Details.ParentModel,
 	}
 
 	format, err := cmd.Flags().GetString("format")
@@ -324,6 +301,18 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 	opts.Format = format
 
+	keepAlive, err := cmd.Flags().GetString("keepalive")
+	if err != nil {
+		return err
+	}
+	if keepAlive != "" {
+		d, err := time.ParseDuration(keepAlive)
+		if err != nil {
+			return err
+		}
+		opts.KeepAlive = &api.Duration{Duration: d}
+	}
+
 	prompts := args[1:]
 	// prepend stdin to the prompt if provided
 	if !term.IsTerminal(int(os.Stdin.Fd())) {
@@ -347,12 +336,54 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 	opts.WordWrap = !nowrap
 
-	if !interactive {
-		return generate(cmd, opts)
+	// Fill out the rest of the options based on information about the
+	// model.
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	name := args[0]
+	info, err := func() (*api.ShowResponse, error) {
+		showReq := &api.ShowRequest{Name: name}
+		info, err := client.Show(cmd.Context(), showReq)
+		var se api.StatusError
+		if errors.As(err, &se) && se.StatusCode == http.StatusNotFound {
+			if err := PullHandler(cmd, []string{name}); err != nil {
+				return nil, err
+			}
+			return client.Show(cmd.Context(), &api.ShowRequest{Name: name})
+		}
+		return info, err
+	}()
+	if err != nil {
+		return err
+	}
+
+	opts.MultiModal = slices.Contains(info.Details.Families, "clip")
+	opts.ParentModel = info.Details.ParentModel
+
+	if interactive {
+		if err := loadModel(cmd, &opts); err != nil {
+			return err
+		}
+
+		for _, msg := range info.Messages {
+			switch msg.Role {
+			case "user":
+				fmt.Printf(">>> %s\n", msg.Content)
+			case "assistant":
+				state := &displayResponseState{}
+				displayResponse(msg.Content, opts.WordWrap, state)
+				fmt.Println()
+				fmt.Println()
+			}
 		}
 
 		return generateInteractive(cmd, opts)
 	}
+	return generate(cmd, opts)
+}
 
 func errFromUnknownKey(unknownKeyErr error) error {
 	// find SSH public key in the error message
@@ -496,6 +527,52 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 	return nil
 }
 
+func ListRunningHandler(cmd *cobra.Command, args []string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	models, err := client.ListRunning(cmd.Context())
+	if err != nil {
+		return err
+	}
+
+	var data [][]string
+
+	for _, m := range models.Models {
+		if len(args) == 0 || strings.HasPrefix(m.Name, args[0]) {
+			var procStr string
+			switch {
+			case m.SizeVRAM == 0:
+				procStr = "100% CPU"
+			case m.SizeVRAM == m.Size:
+				procStr = "100% GPU"
+			case m.SizeVRAM > m.Size || m.Size == 0:
+				procStr = "Unknown"
+			default:
+				sizeCPU := m.Size - m.SizeVRAM
+				cpuPercent := math.Round(float64(sizeCPU) / float64(m.Size) * 100)
+				procStr = fmt.Sprintf("%d%%/%d%% CPU/GPU", int(cpuPercent), int(100-cpuPercent))
+			}
+			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), procStr, format.HumanTime(m.ExpiresAt, "Never")})
+		}
+	}
+
+	table := tablewriter.NewWriter(os.Stdout)
+	table.SetHeader([]string{"NAME", "ID", "SIZE", "PROCESSOR", "UNTIL"})
+	table.SetHeaderAlignment(tablewriter.ALIGN_LEFT)
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+	table.SetHeaderLine(false)
+	table.SetBorder(false)
+	table.SetNoWhiteSpace(true)
+	table.SetTablePadding("\t")
+	table.AppendBulk(data)
+	table.Render()
+
+	return nil
+}
+
 func DeleteHandler(cmd *cobra.Command, args []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -518,10 +595,6 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	if len(args) != 1 {
-		return errors.New("missing model name")
-	}
-
 	license, errLicense := cmd.Flags().GetBool("license")
 	modelfile, errModelfile := cmd.Flags().GetBool("modelfile")
 	parameters, errParams := cmd.Flags().GetBool("parameters")
@@ -564,8 +637,6 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 
 	if flagsSet > 1 {
 		return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
-	} else if flagsSet == 0 {
-		return errors.New("one of '--license', '--modelfile', '--parameters', '--system', or '--template' must be specified")
 	}
 
 	req := api.ShowRequest{Name: args[0]}
@@ -574,6 +645,7 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
+	if flagsSet == 1 {
 		switch showType {
 		case "license":
 			fmt.Println(resp.License)
@@ -590,6 +662,124 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}
 
+	showInfo(resp)
+
+	return nil
+}
+
+func showInfo(resp *api.ShowResponse) {
+	arch := resp.ModelInfo["general.architecture"].(string)
+
+	modelData := [][]string{
+		{"arch", arch},
+		{"parameters", resp.Details.ParameterSize},
+		{"quantization", resp.Details.QuantizationLevel},
+		{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
+		{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
+	}
+
+	mainTableData := [][]string{
+		{"Model"},
+		{renderSubTable(modelData, false)},
+	}
+
+	if resp.ProjectorInfo != nil {
+		projectorData := [][]string{
+			{"arch", "clip"},
+			{"parameters", format.HumanNumber(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))},
+		}
+
+		if projectorType, ok := resp.ProjectorInfo["clip.projector_type"]; ok {
+			projectorData = append(projectorData, []string{"projector type", projectorType.(string)})
+		}
+
+		projectorData = append(projectorData,
+			[]string{"embedding length", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.embedding_length"].(float64))},
+			[]string{"projection dimensionality", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.projection_dim"].(float64))},
+		)
+
+		mainTableData = append(mainTableData,
+			[]string{"Projector"},
+			[]string{renderSubTable(projectorData, false)},
+		)
+	}
+
+	if resp.Parameters != "" {
+		mainTableData = append(mainTableData, []string{"Parameters"}, []string{formatParams(resp.Parameters)})
+	}
+
+	if resp.System != "" {
+		mainTableData = append(mainTableData, []string{"System"}, []string{renderSubTable(twoLines(resp.System), true)})
+	}
+
+	if resp.License != "" {
+		mainTableData = append(mainTableData, []string{"License"}, []string{renderSubTable(twoLines(resp.License), true)})
+	}
+
+	table := tablewriter.NewWriter(os.Stdout)
+	table.SetAutoWrapText(false)
+	table.SetBorder(false)
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+
+	for _, v := range mainTableData {
+		table.Append(v)
+	}
+
+	table.Render()
+}
+
+func renderSubTable(data [][]string, file bool) string {
+	var buf bytes.Buffer
+	table := tablewriter.NewWriter(&buf)
+	table.SetAutoWrapText(!file)
+	table.SetBorder(false)
+	table.SetNoWhiteSpace(true)
+	table.SetTablePadding("\t")
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+
+	for _, v := range data {
+		table.Append(v)
+	}
+
+	table.Render()
+
+	renderedTable := buf.String()
+	lines := strings.Split(renderedTable, "\n")
+	for i, line := range lines {
+		lines[i] = "\t" + line
+	}
+
+	return strings.Join(lines, "\n")
+}
+
+func twoLines(s string) [][]string {
+	lines := strings.Split(s, "\n")
+	res := [][]string{}
+
+	count := 0
+	for _, line := range lines {
+		line = strings.TrimSpace(line)
+		if line != "" {
+			count++
+			res = append(res, []string{line})
+			if count == 2 {
+				return res
+			}
+		}
+	}
+	return res
+}
+
+func formatParams(s string) string {
+	lines := strings.Split(s, "\n")
+	table := [][]string{}
+
+	for _, line := range lines {
+		table = append(table, strings.Fields(line))
+	}
+	return renderSubTable(table, false)
+}
+
 func CopyHandler(cmd *cobra.Command, args []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -668,10 +858,10 @@ type runOptions struct {
 	WordWrap    bool
 	Format      string
 	System      string
-	Template    string
 	Images      []api.ImageData
 	Options     map[string]interface{}
 	MultiModal  bool
+	KeepAlive   *api.Duration
 }
 
 type displayResponseState struct {
@@ -684,7 +874,7 @@ func displayResponse(content string, wordWrap bool, state *displayResponseState)
 	if wordWrap && termWidth >= 10 {
 		for _, ch := range content {
 			if state.lineLength+1 > termWidth-5 {
-				if len(state.wordBuffer) > termWidth-10 {
+				if runewidth.StringWidth(state.wordBuffer) > termWidth-10 {
 					fmt.Printf("%s%c", state.wordBuffer, ch)
 					state.wordBuffer = ""
 					state.lineLength = 0
@@ -692,12 +882,22 @@ func displayResponse(content string, wordWrap bool, state *displayResponseState)
 				}
 
 				// backtrack the length of the last word and clear to the end of the line
-				fmt.Printf("\x1b[%dD\x1b[K\n", len(state.wordBuffer))
+				a := runewidth.StringWidth(state.wordBuffer)
+				if a > 0 {
+					fmt.Printf("\x1b[%dD", a)
+				}
+				fmt.Printf("\x1b[K\n")
 				fmt.Printf("%s%c", state.wordBuffer, ch)
-				state.lineLength = len(state.wordBuffer) + 1
+				chWidth := runewidth.RuneWidth(ch)
+
+				state.lineLength = runewidth.StringWidth(state.wordBuffer) + chWidth
 			} else {
 				fmt.Print(string(ch))
-				state.lineLength += 1
+				state.lineLength += runewidth.RuneWidth(ch)
+				if runewidth.RuneWidth(ch) >= 2 {
+					state.wordBuffer = ""
+					continue
+				}
 
 				switch ch {
 				case ' ':
@@ -766,6 +966,10 @@ func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 		Options:  opts.Options,
 	}
 
+	if opts.KeepAlive != nil {
+		req.KeepAlive = opts.KeepAlive
+	}
+
 	if err := client.Chat(cancelCtx, req, fn); err != nil {
 		if errors.Is(err, context.Canceled) {
 			return nil, nil
@@ -847,8 +1051,8 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 		Images:    opts.Images,
 		Format:    opts.Format,
 		System:    opts.System,
-		Template: opts.Template,
 		Options:   opts.Options,
+		KeepAlive: opts.KeepAlive,
 	}
 
 	if err := client.Generate(ctx, &request, fn); err != nil {
@@ -883,17 +1087,11 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 }
 
 func RunServer(cmd *cobra.Command, _ []string) error {
-	// retrieve the OLLAMA_HOST environment variable
-	ollamaHost, err := api.GetOllamaHost()
-	if err != nil {
-		return err
-	}
-
 	if err := initializeKeypair(); err != nil {
 		return err
 	}
 
-	ln, err := net.Listen("tcp", net.JoinHostPort(ollamaHost.Host, ollamaHost.Port))
+	ln, err := net.Listen("tcp", envconfig.Host().Host)
 	if err != nil {
 		return err
 	}
@@ -952,24 +1150,6 @@ func initializeKeypair() error {
 	return nil
 }
 
-//nolint:unused
-func waitForServer(ctx context.Context, client *api.Client) error {
-	// wait for the server to start
-	timeout := time.After(5 * time.Second)
-	tick := time.Tick(500 * time.Millisecond)
-	for {
-		select {
-		case <-timeout:
-			return errors.New("timed out waiting for server to start")
-		case <-tick:
-			if err := client.Heartbeat(ctx); err == nil {
-				return nil // server has started
-			}
-		}
-	}
-
-}
-
 func checkServerHeartbeat(cmd *cobra.Command, _ []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -1006,12 +1186,19 @@ func versionHandler(cmd *cobra.Command, _ []string) {
 	}
 }
 
-func appendHostEnvDocs(cmd *cobra.Command) {
-	const hostEnvDocs = `
+func appendEnvDocs(cmd *cobra.Command, envs []envconfig.EnvVar) {
+	if len(envs) == 0 {
+		return
+	}
+
+	envUsage := `
 Environment Variables:
-      OLLAMA_HOST        The host:port or base URL of the Ollama server (e.g. http://localhost:11434)
 `
-	cmd.SetUsageTemplate(cmd.UsageTemplate() + hostEnvDocs)
+	for _, e := range envs {
+		envUsage += fmt.Sprintf("      %-24s   %s\n", e.Name, e.Description)
+	}
+
+	cmd.SetUsageTemplate(cmd.UsageTemplate() + envUsage)
 }
 
 func NewCLI() *cobra.Command {
@@ -1050,7 +1237,7 @@ func NewCLI() *cobra.Command {
 		RunE:    CreateHandler,
 	}
 
-	createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile (default \"Modelfile\")")
+	createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_0)")
 
 	showCmd := &cobra.Command{
@@ -1075,6 +1262,7 @@ func NewCLI() *cobra.Command {
 		RunE:    RunHandler,
 	}
 
+	runCmd.Flags().String("keepalive", "", "Duration to keep a model loaded (e.g. 5m)")
 	runCmd.Flags().Bool("verbose", false, "Show timings for response")
 	runCmd.Flags().Bool("insecure", false, "Use an insecure registry")
 	runCmd.Flags().Bool("nowordwrap", false, "Don't wrap words to the next line automatically")
@@ -1086,15 +1274,6 @@ func NewCLI() *cobra.Command {
 		Args:    cobra.ExactArgs(0),
 		RunE:    RunServer,
 	}
-	serveCmd.SetUsageTemplate(serveCmd.UsageTemplate() + `
-Environment Variables:
-
-    OLLAMA_HOST         The host:port to bind to (default "127.0.0.1:11434")
-    OLLAMA_ORIGINS      A comma separated list of allowed origins.
-    OLLAMA_MODELS       The path to the models directory (default is "~/.ollama/models")
-    OLLAMA_KEEP_ALIVE   The duration that models stay loaded in memory (default is "5m")
-    OLLAMA_DEBUG        Set to 1 to enable additional debug logging
-`)
 
 	pullCmd := &cobra.Command{
 		Use:     "pull MODEL",
@@ -1123,6 +1302,14 @@ Environment Variables:
 		PreRunE: checkServerHeartbeat,
 		RunE:    ListHandler,
 	}
+
+	psCmd := &cobra.Command{
+		Use:     "ps",
+		Short:   "List running models",
+		PreRunE: checkServerHeartbeat,
+		RunE:    ListRunningHandler,
+	}
+
 	copyCmd := &cobra.Command{
 		Use:     "cp SOURCE DESTINATION",
 		Short:   "Copy a model",
@@ -1139,6 +1326,10 @@ Environment Variables:
 		RunE:    DeleteHandler,
 	}
 
+	envVars := envconfig.AsMap()
+
+	envs := []envconfig.EnvVar{envVars["OLLAMA_HOST"]}
+
 	for _, cmd := range []*cobra.Command{
 		createCmd,
 		showCmd,
@@ -1146,10 +1337,33 @@ Environment Variables:
 		pullCmd,
 		pushCmd,
 		listCmd,
+		psCmd,
 		copyCmd,
 		deleteCmd,
+		serveCmd,
 	} {
-		appendHostEnvDocs(cmd)
+		switch cmd {
+		case runCmd:
+			appendEnvDocs(cmd, []envconfig.EnvVar{envVars["OLLAMA_HOST"], envVars["OLLAMA_NOHISTORY"]})
+		case serveCmd:
+			appendEnvDocs(cmd, []envconfig.EnvVar{
+				envVars["OLLAMA_DEBUG"],
+				envVars["OLLAMA_HOST"],
+				envVars["OLLAMA_KEEP_ALIVE"],
+				envVars["OLLAMA_MAX_LOADED_MODELS"],
+				envVars["OLLAMA_MAX_QUEUE"],
+				envVars["OLLAMA_MODELS"],
+				envVars["OLLAMA_NUM_PARALLEL"],
+				envVars["OLLAMA_NOPRUNE"],
+				envVars["OLLAMA_ORIGINS"],
+				envVars["OLLAMA_SCHED_SPREAD"],
+				envVars["OLLAMA_TMPDIR"],
+				envVars["OLLAMA_FLASH_ATTENTION"],
+				envVars["OLLAMA_LLM_LIBRARY"],
+			})
+		default:
+			appendEnvDocs(cmd, envs)
+		}
 	}
 
 	rootCmd.AddCommand(
@@ -1160,6 +1374,7 @@ Environment Variables:
 		pullCmd,
 		pushCmd,
 		listCmd,
+		psCmd,
 		copyCmd,
 		deleteCmd,
 	)

cmd/interactive.go

@@ -1,6 +1,7 @@
 package cmd
 
 import (
+	"cmp"
 	"errors"
 	"fmt"
 	"io"
@@ -8,15 +9,18 @@ import (
 	"os"
 	"path/filepath"
 	"regexp"
-	"sort"
+	"slices"
 	"strings"
 
 	"github.com/spf13/cobra"
-	"golang.org/x/exp/slices"
+	"golang.org/x/exp/maps"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/readline"
+	"github.com/ollama/ollama/types/errtypes"
 )
 
 type MultilineState int
@@ -25,69 +29,29 @@ const (
 	MultilineNone MultilineState = iota
 	MultilinePrompt
 	MultilineSystem
-	MultilineTemplate
 )
 
 func loadModel(cmd *cobra.Command, opts *runOptions) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
 	p := progress.NewProgress(os.Stderr)
 	defer p.StopAndClear()
 
 	spinner := progress.NewSpinner("")
 	p.Add("", spinner)
 
-	showReq := api.ShowRequest{Name: opts.Model}
-	showResp, err := client.Show(cmd.Context(), &showReq)
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
-	opts.MultiModal = slices.Contains(showResp.Details.Families, "clip")
-	opts.ParentModel = showResp.Details.ParentModel
-
-	if len(showResp.Messages) > 0 {
-		opts.Messages = append(opts.Messages, showResp.Messages...)
-	}
 
 	chatReq := &api.ChatRequest{
 		Model:     opts.Model,
-		Messages: []api.Message{},
-	}
-	err = client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
-		p.StopAndClear()
-		if len(opts.Messages) > 0 {
-			for _, msg := range opts.Messages {
-				switch msg.Role {
-				case "user":
-					fmt.Printf(">>> %s\n", msg.Content)
-				case "assistant":
-					state := &displayResponseState{}
-					displayResponse(msg.Content, opts.WordWrap, state)
-					fmt.Println()
-					fmt.Println()
-				}
-			}
-		}
-		return nil
-	})
-	if err != nil {
-		return err
+		KeepAlive: opts.KeepAlive,
 	}
 
-	return nil
+	return client.Chat(cmd.Context(), chatReq, func(api.ChatResponse) error { return nil })
 }
 
 func generateInteractive(cmd *cobra.Command, opts runOptions) error {
-	opts.Messages = make([]api.Message, 0)
-
-	err := loadModel(cmd, &opts)
-	if err != nil {
-		return err
-	}
-
 	usage := func() {
 		fmt.Fprintln(os.Stderr, "Available Commands:")
 		fmt.Fprintln(os.Stderr, "  /set            Set session variables")
@@ -112,7 +76,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "Available Commands:")
 		fmt.Fprintln(os.Stderr, "  /set parameter ...     Set a parameter")
 		fmt.Fprintln(os.Stderr, "  /set system <string>   Set system message")
-		fmt.Fprintln(os.Stderr, "  /set template <string> Set prompt template")
 		fmt.Fprintln(os.Stderr, "  /set history           Enable history")
 		fmt.Fprintln(os.Stderr, "  /set nohistory         Disable history")
 		fmt.Fprintln(os.Stderr, "  /set wordwrap          Enable wordwrap")
@@ -132,6 +95,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "   Alt + f            Move forward (right) one word")
 		fmt.Fprintln(os.Stderr, "  Ctrl + k            Delete the sentence after the cursor")
 		fmt.Fprintln(os.Stderr, "  Ctrl + u            Delete the sentence before the cursor")
+		fmt.Fprintln(os.Stderr, "  Ctrl + w            Delete the word before the cursor")
 		fmt.Fprintln(os.Stderr, "")
 		fmt.Fprintln(os.Stderr, "  Ctrl + l            Clear the screen")
 		fmt.Fprintln(os.Stderr, "  Ctrl + c            Stop the model from responding")
@@ -157,6 +121,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "  /set parameter num_predict <int>      Max number of tokens to predict")
 		fmt.Fprintln(os.Stderr, "  /set parameter top_k <int>            Pick from top k num of tokens")
 		fmt.Fprintln(os.Stderr, "  /set parameter top_p <float>          Pick token based on sum of probabilities")
+		fmt.Fprintln(os.Stderr, "  /set parameter min_p <float>          Pick token based on top token probability * min_p")
 		fmt.Fprintln(os.Stderr, "  /set parameter num_ctx <int>          Set the context size")
 		fmt.Fprintln(os.Stderr, "  /set parameter temperature <float>    Set creativity level")
 		fmt.Fprintln(os.Stderr, "  /set parameter repeat_penalty <float> How strongly to penalize repetitions")
@@ -176,6 +141,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		return err
 	}
 
+	if envconfig.NoHistory() {
+		scanner.HistoryDisable()
+	}
+
 	fmt.Print(readline.StartBracketedPaste)
 	defer fmt.Printf(readline.EndBracketedPaste)
 
@@ -217,10 +186,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				opts.Messages = append(opts.Messages, api.Message{Role: "system", Content: opts.System})
 				fmt.Println("Set system message.")
 				sb.Reset()
-			case MultilineTemplate:
-				opts.Template = sb.String()
-				fmt.Println("Set prompt template.")
-				sb.Reset()
 			}
 
 			multiline = MultilineNone
@@ -276,13 +241,20 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 			fn := func(resp api.ProgressResponse) error { return nil }
 			err = client.Create(cmd.Context(), req, fn)
 			if err != nil {
-				fmt.Println("error: couldn't save model")
+				if strings.Contains(err.Error(), errtypes.InvalidModelNameErrMsg) {
+					fmt.Printf("error: The model name '%s' is invalid\n", args[1])
+					continue
+				}
 				return err
 			}
 			fmt.Printf("Created new model '%s'\n", args[1])
 			continue
 		case strings.HasPrefix(line, "/clear"):
 			opts.Messages = []api.Message{}
+			if opts.System != "" {
+				newMessage := api.Message{Role: "system", Content: opts.System}
+				opts.Messages = append(opts.Messages, newMessage)
+			}
 			fmt.Println("Cleared session context")
 			continue
 		case strings.HasPrefix(line, "/set"):
@@ -332,17 +304,13 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					}
 					fmt.Printf("Set parameter '%s' to '%s'\n", args[2], strings.Join(params, ", "))
 					opts.Options[args[2]] = fp[args[2]]
-				case "system", "template":
+				case "system":
 					if len(args) < 3 {
 						usageSet()
 						continue
 					}
 
-					if args[1] == "system" {
 					multiline = MultilineSystem
-					} else if args[1] == "template" {
-						multiline = MultilineTemplate
-					}
 
 					line := strings.Join(args[2:], " ")
 					line, ok := strings.CutPrefix(line, `"""`)
@@ -362,7 +330,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						continue
 					}
 
-					if args[1] == "system" {
 					opts.System = sb.String() // for display in modelfile
 					newMessage := api.Message{Role: "system", Content: sb.String()}
 					// Check if the slice is not empty and the last message is from 'system'
@@ -374,11 +341,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					}
 					fmt.Println("Set system message.")
 					sb.Reset()
-					} else if args[1] == "template" {
-						opts.Template = sb.String()
-						fmt.Println("Set prompt template.")
-						sb.Reset()
-					}
 
 					sb.Reset()
 					continue
@@ -399,7 +361,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				req := &api.ShowRequest{
 					Name:    opts.Model,
 					System:  opts.System,
-					Template: opts.Template,
 					Options: opts.Options,
 				}
 				resp, err := client.Show(cmd.Context(), req)
@@ -410,15 +371,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					fmt.Println("Model details:")
-					if len(resp.Details.Families) > 0 {
-						fmt.Printf("Family              %s\n", strings.Join(resp.Details.Families, ", "))
-					} else if resp.Details.Family != "" {
-						fmt.Printf("Family              %s\n", resp.Details.Family)
-					}
-					fmt.Printf("Parameter Size      %s\n", resp.Details.ParameterSize)
-					fmt.Printf("Quantization Level  %s\n", resp.Details.QuantizationLevel)
-					fmt.Println("")
+					showInfo(resp)
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")
@@ -451,12 +404,9 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						fmt.Println("No system message was specified for this model.")
 					}
 				case "template":
-					switch {
-					case opts.Template != "":
-						fmt.Println(opts.Template + "\n")
-					case resp.Template != "":
+					if resp.Template != "" {
 						fmt.Println(resp.Template)
-					default:
+					} else {
 						fmt.Println("No prompt template was specified for this model.")
 					}
 				default:
@@ -540,35 +490,35 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func buildModelfile(opts runOptions) string {
-	var mf strings.Builder
-	model := opts.ParentModel
-	if model == "" {
-		model = opts.Model
-	}
-	fmt.Fprintf(&mf, "FROM %s\n", model)
+	var f parser.File
+	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
+
 	if opts.System != "" {
-		fmt.Fprintf(&mf, "SYSTEM \"\"\"%s\"\"\"\n", opts.System)
+		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
 	}
 
-	if opts.Template != "" {
-		fmt.Fprintf(&mf, "TEMPLATE \"\"\"%s\"\"\"\n", opts.Template)
-	}
-
-	keys := make([]string, 0)
-	for k := range opts.Options {
-		keys = append(keys, k)
-	}
-	sort.Strings(keys)
+	keys := maps.Keys(opts.Options)
+	slices.Sort(keys)
 	for _, k := range keys {
-		fmt.Fprintf(&mf, "PARAMETER %s %v\n", k, opts.Options[k])
+		v := opts.Options[k]
+		var cmds []parser.Command
+		switch t := v.(type) {
+		case []string:
+			for _, s := range t {
+				cmds = append(cmds, parser.Command{Name: k, Args: s})
+			}
+		default:
+			cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
+		}
+
+		f.Commands = append(f.Commands, cmds...)
 	}
-	fmt.Fprintln(&mf)
 
 	for _, msg := range opts.Messages {
-		fmt.Fprintf(&mf, "MESSAGE %s \"\"\"%s\"\"\"\n", msg.Role, msg.Content)
+		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
 	}
 
-	return mf.String()
+	return f.String()
 }
 
 func normalizeFilePath(fp string) string {

cmd/interactive_test.go

@@ -1,10 +1,9 @@
 package cmd
 
 import (
-	"bytes"
 	"testing"
-	"text/template"
 
+	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
 
 	"github.com/ollama/ollama/api"
@@ -58,59 +57,51 @@ func TestModelfileBuilder(t *testing.T) {
 	opts := runOptions{
 		Model:  "hork",
 		System: "You are part horse and part shark, but all hork. Do horklike things",
-		Template: "This is a template.",
 		Messages: []api.Message{
 			{Role: "user", Content: "Hey there hork!"},
 			{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
 		},
-		Options: map[string]interface{}{},
+		Options: map[string]any{
+			"temperature":      0.9,
+			"seed":             42,
+			"penalize_newline": false,
+			"stop":             []string{"hi", "there"},
+		},
 	}
 
-	opts.Options["temperature"] = 0.9
-	opts.Options["seed"] = 42
-	opts.Options["penalize_newline"] = false
-	opts.Options["stop"] = []string{"hi", "there"}
-
-	mf := buildModelfile(opts)
-	expectedModelfile := `FROM {{.Model}}
-SYSTEM """{{.System}}"""
-TEMPLATE """{{.Template}}"""
+	t.Run("model", func(t *testing.T) {
+		expect := `FROM hork
+SYSTEM You are part horse and part shark, but all hork. Do horklike things
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop [hi there]
+PARAMETER stop hi
+PARAMETER stop there
 PARAMETER temperature 0.9
-
-MESSAGE user """Hey there hork!"""
-MESSAGE assistant """Yes it is true, I am half horse, half shark."""
+MESSAGE user Hey there hork!
+MESSAGE assistant Yes it is true, I am half horse, half shark.
 `
 
-	tmpl, err := template.New("").Parse(expectedModelfile)
-	assert.Nil(t, err)
-
-	var buf bytes.Buffer
-	err = tmpl.Execute(&buf, opts)
-	assert.Nil(t, err)
-	assert.Equal(t, buf.String(), mf)
+		actual := buildModelfile(opts)
+		if diff := cmp.Diff(expect, actual); diff != "" {
+			t.Errorf("mismatch (-want +got):\n%s", diff)
+		}
+	})
 
+	t.Run("parent model", func(t *testing.T) {
 		opts.ParentModel = "horseshark"
-	mf = buildModelfile(opts)
-	expectedModelfile = `FROM {{.ParentModel}}
-SYSTEM """{{.System}}"""
-TEMPLATE """{{.Template}}"""
+		expect := `FROM horseshark
+SYSTEM You are part horse and part shark, but all hork. Do horklike things
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop [hi there]
+PARAMETER stop hi
+PARAMETER stop there
 PARAMETER temperature 0.9
-
-MESSAGE user """Hey there hork!"""
-MESSAGE assistant """Yes it is true, I am half horse, half shark."""
+MESSAGE user Hey there hork!
+MESSAGE assistant Yes it is true, I am half horse, half shark.
 `
-
-	tmpl, err = template.New("").Parse(expectedModelfile)
-	assert.Nil(t, err)
-
-	var parentBuf bytes.Buffer
-	err = tmpl.Execute(&parentBuf, opts)
-	assert.Nil(t, err)
-	assert.Equal(t, parentBuf.String(), mf)
+		actual := buildModelfile(opts)
+		if diff := cmp.Diff(expect, actual); diff != "" {
+			t.Errorf("mismatch (-want +got):\n%s", diff)
+		}
+	})
 }

cmd/start.go

@@ -0,0 +1,27 @@
+//go:build darwin || windows
+
+package cmd
+
+import (
+	"context"
+	"errors"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+func waitForServer(ctx context.Context, client *api.Client) error {
+	// wait for the server to start
+	timeout := time.After(5 * time.Second)
+	tick := time.Tick(500 * time.Millisecond)
+	for {
+		select {
+		case <-timeout:
+			return errors.New("timed out waiting for server to start")
+		case <-tick:
+			if err := client.Heartbeat(ctx); err == nil {
+				return nil // server has started
+			}
+		}
+	}
+}

convert/convert.go

@@ -1,188 +1,122 @@
 package convert
 
 import (
-	"cmp"
-	"encoding/binary"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"io"
+	"io/fs"
 	"log/slog"
-	"os"
-	"path/filepath"
-	"slices"
-	"strings"
 
-	"google.golang.org/protobuf/proto"
-
-	"github.com/ollama/ollama/convert/sentencepiece"
 	"github.com/ollama/ollama/llm"
 )
 
-type Params struct {
+type Parameters struct {
 	Architectures []string `json:"architectures"`
-	VocabSize         int      `json:"vocab_size"`
-	HiddenSize        int      `json:"hidden_size"`       // n_embd
-	HiddenLayers      int      `json:"num_hidden_layers"` // n_layer
-	ContextSize       int      `json:"max_position_embeddings"`
-	IntermediateSize  int      `json:"intermediate_size"`
-	AttentionHeads    int      `json:"num_attention_heads"` // n_head
-	KeyValHeads       int      `json:"num_key_value_heads"`
-	NormEPS           float64  `json:"rms_norm_eps"`
-	BoSTokenID        int      `json:"bos_token_id"`
-	EoSTokenID        int      `json:"eos_token_id"`
-	HeadDimension     int      `json:"head_dim"`
-	PaddingTokenID    int      `json:"pad_token_id"`
-	RopeFrequencyBase float64  `json:"rope_theta"`
-
-	Experts     int `json:"num_local_experts"`
-	ExpertsUsed int `json:"num_experts_per_tok"`
-
-	ByteOrder
+	VocabSize     uint32   `json:"vocab_size"`
 }
 
-type ByteOrder interface {
-	binary.ByteOrder
-	binary.AppendByteOrder
+func (Parameters) KV(t *Tokenizer) llm.KV {
+	kv := llm.KV{
+		"general.file_type":            uint32(1),
+		"general.quantization_version": uint32(2),
+		"tokenizer.ggml.pre":           t.Pre,
+		"tokenizer.ggml.model":         t.Vocabulary.Model,
+		"tokenizer.ggml.tokens":        t.Vocabulary.Tokens,
+		"tokenizer.ggml.scores":        t.Vocabulary.Scores,
+		"tokenizer.ggml.token_type":    t.Vocabulary.Types,
 	}
 
-type ModelArch interface {
-	GetTensors() error
-	LoadVocab() error
-	WriteGGUF(io.WriteSeeker) error
+	if t.Template != "" {
+		kv["tokenizer.chat_template"] = t.Template
 	}
 
-type ModelFormat interface {
-	GetLayerName(string) (string, error)
-	GetTensors(string, *Params) ([]llm.Tensor, error)
-	GetParams(string) (*Params, error)
-	GetModelArch(string, string, *Params) (ModelArch, error)
+	for _, sv := range t.SpecialVocabulary {
+		kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
+		kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
 	}
 
-type ModelData struct {
-	Path    string
-	Name    string
-	Params  *Params
-	Vocab   *Vocab
-	Tensors []llm.Tensor
-	Format  ModelFormat
+	return kv
 }
 
-func GetModelFormat(dirname string) (ModelFormat, error) {
-	files, err := filepath.Glob(filepath.Join(dirname, "*"))
+func (Parameters) specialTokenTypes() []string {
+	return []string{
+		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
+	}
+}
+
+func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
+	return llm.WriteGGUF(ws, kv, ts)
+}
+
+type Converter interface {
+	// KV maps parameters to LLM key-values
+	KV(*Tokenizer) llm.KV
+	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
+	Tensors([]Tensor) []llm.Tensor
+
+	// tensorName returns the LLM tensor name for a specific input name
+	tensorName(string) string
+	// specialTokenTypes returns any special token types the model uses
+	specialTokenTypes() []string
+	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+}
+
+// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
+// and files it finds in the input path.
+// Supported input model formats include safetensors.
+// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
+func Convert(fsys fs.FS, ws io.WriteSeeker) error {
+	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
-		return nil, err
+		return err
 	}
 
-	for _, fn := range files {
-		slog.Debug(fmt.Sprintf("file = %s", fn))
-		if strings.HasSuffix(fn, ".safetensors") {
-			return &SafetensorFormat{}, nil
-		} else if strings.HasSuffix(fn, ".bin") {
-			slog.Debug("model is torch")
-			return &TorchFormat{}, nil
-		}
+	var p Parameters
+	if err := json.Unmarshal(bts, &p); err != nil {
+		return err
 	}
 
-	return nil, fmt.Errorf("couldn't determine model format")
+	if len(p.Architectures) < 1 {
+		return errors.New("unknown architecture")
 	}
 
-// Details on gguf's tokenizer can be found at:
-// https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#tokenizer
-type Vocab struct {
-	Tokens []string
-	Scores []float32
-	Types  []int32
-}
-
-func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
-	slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
-	in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
-	if err != nil {
-		return nil, err
-	}
-
-	// To regenerate sentencepiece from the protobufs use:
-	// protoc -I=./ --go_out=./ sentencepiece_model.proto
-	modelProto := &sentencepiece.ModelProto{}
-	if err := proto.Unmarshal(in, modelProto); err != nil {
-		return nil, err
-	}
-
-	v := &Vocab{
-		Tokens: make([]string, 0),
-		Scores: make([]float32, 0),
-		Types:  make([]int32, 0),
-	}
-
-	pieces := modelProto.GetPieces()
-	for _, p := range pieces {
-		v.Tokens = append(v.Tokens, p.GetPiece())
-		v.Scores = append(v.Scores, p.GetScore())
-		t := p.GetType()
-		switch t {
-		case sentencepiece.ModelProto_SentencePiece_UNKNOWN:
-		case sentencepiece.ModelProto_SentencePiece_CONTROL:
-		case sentencepiece.ModelProto_SentencePiece_UNUSED:
-		case sentencepiece.ModelProto_SentencePiece_BYTE:
+	var conv Converter
+	switch p.Architectures[0] {
+	case "LlamaForCausalLM", "MistralForCausalLM":
+		conv = &llama{}
+	case "MixtralForCausalLM":
+		conv = &mixtral{}
+	case "GemmaForCausalLM":
+		conv = &gemma{}
 	default:
-			t = sentencepiece.ModelProto_SentencePiece_NORMAL
-		}
-		v.Types = append(v.Types, int32(t))
+		return errors.New("unsupported architecture")
 	}
 
-	slog.Info(fmt.Sprintf("vocab size: %d", len(v.Tokens)))
-
-	// add any additional tokens
-	addIn, err := os.ReadFile(filepath.Join(dirpath, "added_tokens.json"))
-	if os.IsNotExist(err) {
-		return v, nil
-	} else if err != nil {
-		return nil, err
+	if err := json.Unmarshal(bts, conv); err != nil {
+		return err
 	}
 
-	slog.Info("reading user defined tokens")
-
-	var extraTokenData map[string]int
-	if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
-		return nil, err
+	t, err := parseTokenizer(fsys, conv.specialTokenTypes())
+	if err != nil {
+		return err
 	}
 
-	type token struct {
-		key string
-		pos int
+	if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
+		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
+		for i := range vocabSize - len(t.Vocabulary.Tokens) {
+			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
+			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
+			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
+		}
+	} else {
+		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
 
-	extraTokens := make([]token, 0)
-	for k, id := range extraTokenData {
-		extraTokens = append(extraTokens, token{k, id})
+	ts, err := parseTensors(fsys)
+	if err != nil {
+		return err
 	}
 
-	slices.SortFunc(extraTokens, func(a, b token) int {
-		return cmp.Compare(a.pos, b.pos)
-	})
-
-	numToks := len(v.Tokens)
-
-	for cnt, t := range extraTokens {
-		// the token id should match the specific index for the total number of tokens
-		if t.pos != cnt+numToks {
-			return nil, fmt.Errorf("token ID '%d' for '%s' doesn't match total token size", t.pos, t.key)
-		}
-		v.Tokens = append(v.Tokens, t.key)
-		v.Scores = append(v.Scores, -1000.0)
-		v.Types = append(v.Types, int32(llm.GGUFTokenUserDefined))
-	}
-	slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
-
-	if params.VocabSize > len(v.Tokens) {
-		missingTokens := params.VocabSize - len(v.Tokens)
-		slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
-		for cnt := 0; cnt < missingTokens; cnt++ {
-			v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
-			v.Scores = append(v.Scores, -1)
-			v.Types = append(v.Types, int32(llm.GGUFTokenUserDefined))
-		}
-	}
-
-	return v, nil
+	return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
 }

convert/convert_gemma.go

@@ -0,0 +1,103 @@
+package convert
+
+import (
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type gemma struct {
+	Parameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RMSNormEPS            float32 `json:"rms_norm_eps"`
+	HeadDim               uint32  `json:"head_dim"`
+}
+
+var _ Converter = (*gemma)(nil)
+
+func (p *gemma) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
+	kv["general.architecture"] = "gemma"
+	kv["general.name"] = "gemma"
+	kv["gemma.context_length"] = p.MaxPositionEmbeddings
+	kv["gemma.embedding_length"] = p.HiddenSize
+	kv["gemma.block_count"] = p.HiddenLayers
+	kv["gemma.feed_forward_length"] = p.IntermediateSize
+	kv["gemma.attention.head_count"] = p.NumAttentionHeads
+	kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["gemma.attention.key_length"] = p.HeadDim
+	kv["gemma.attention.value_length"] = p.HeadDim
+	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
+	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
+	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
+	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
+	return kv
+}
+
+func (p *gemma) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+	for _, t := range ts {
+		name := p.tensorName(t.Name())
+		if strings.HasSuffix(name, "_norm.weight") {
+			t.SetRepacker(p.addOne)
+		}
+
+		out = append(out, llm.Tensor{
+			Name:     name,
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *gemma) tensorName(n string) string {
+	return strings.NewReplacer(
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+		"block_sparse_moe.gate", "ffn_inp",
+	).Replace(n)
+}
+
+func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
+	n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
+	ones := tensor.Ones(tensor.Float32, int(shape[0]))
+
+	n, err := n.Add(ones)
+	if err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 0)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/convert_llama.go

@@ -0,0 +1,182 @@
+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/llm"
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+)
+
+type llama struct {
+	Parameters
+	NLayers               uint32  `json:"n_layers"`
+	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
+	NLayer                uint32  `json:"n_layer"`
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	NCtx                  uint32  `json:"n_ctx"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	NEmbd                 uint32  `json:"n_embd"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NInner                uint32  `json:"n_inner"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NHead                 uint32  `json:"n_head"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type   string  `json:"type"`
+		Factor float32 `json:"factor"`
+	} `json:"rope_scaling"`
+	RMSNormEPS       float32 `json:"rms_norm_eps"`
+	LayerNormEPS     float32 `json:"layer_norm_eps"`
+	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
+	NormEpsilon      float32 `json:"norm_epsilon"`
+	HeadDim          uint32  `json:"head_dim"`
+}
+
+var _ Converter = (*llama)(nil)
+
+func (p *llama) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
+	kv["general.architecture"] = "llama"
+	kv["general.name"] = "llama"
+	kv["llama.vocab_size"] = p.VocabSize
+
+	kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
+
+	if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
+		kv["llama.context_length"] = contextLength
+	}
+
+	if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
+		kv["llama.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
+	}
+
+	if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
+		kv["llama.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
+	}
+
+	if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
+		kv["llama.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
+		kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
+	}
+
+	if p.RopeTheta > 0 {
+		kv["llama.rope.freq_base"] = p.RopeTheta
+	}
+
+	if p.RopeScaling.Type == "linear" {
+		kv["llama.rope.scaling.type"] = p.RopeScaling.Type
+		kv["llama.rope.scaling.factor"] = p.RopeScaling.Factor
+	}
+
+	if p.NumKeyValueHeads > 0 {
+		kv["llama.attention.head_count_kv"] = p.NumKeyValueHeads
+	}
+
+	if p.RMSNormEPS > 0 {
+		kv["llama.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	}
+
+	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
+		kv["llama.attention.layer_norm_epsilon"] = layerNormEpsilon
+	}
+
+	if p.HeadDim > 0 {
+		kv["llama.attention.key_length"] = p.HeadDim
+		kv["llama.attention.value_length"] = p.HeadDim
+	}
+
+	if len(t.Merges) > 0 {
+		kv["tokenizer.ggml.merges"] = t.Merges
+	}
+
+	return kv
+}
+
+func (p *llama) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+	for _, t := range ts {
+		name := p.tensorName(t.Name())
+		if strings.HasSuffix(name, "attn_q.weight") ||
+			strings.HasSuffix(name, "attn_k.weight") {
+			t.SetRepacker(p.repack)
+		}
+
+		out = append(out, llm.Tensor{
+			Name:     name,
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *llama) tensorName(n string) string {
+	return strings.NewReplacer(
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+		// mixtral
+		"block_sparse_moe.gate", "ffn_gate_inp",
+	).Replace(n)
+}
+
+func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		dims = append(dims, int(dim))
+	}
+
+	var heads uint32
+	if strings.HasSuffix(name, "q_proj.weight") {
+		heads = p.NumAttentionHeads
+	} else if strings.HasSuffix(name, "k_proj.weight") {
+		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
+	} else {
+		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
+		return nil, err
+	}
+
+	if err := n.T(0, 2, 1, 3); err != nil {
+		return nil, err
+	}
+
+	if err := n.Reshape(dims...); err != nil {
+		return nil, err
+	}
+
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/convert_mixtral.go

@@ -0,0 +1,89 @@
+package convert
+
+import (
+	"fmt"
+	"io"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type mixtral struct {
+	llama
+	NumLocalExperts    uint32 `json:"num_local_experts"`
+	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
+}
+
+var _ Converter = (*mixtral)(nil)
+
+func (p *mixtral) KV(t *Tokenizer) llm.KV {
+	kv := p.llama.KV(t)
+
+	if p.NumLocalExperts > 0 {
+		kv["llama.expert_count"] = p.NumLocalExperts
+	}
+
+	if p.NumExpertsPerToken > 0 {
+		kv["llama.expert_used_count"] = p.NumExpertsPerToken
+	}
+
+	return kv
+}
+
+func (p *mixtral) Tensors(ts []Tensor) []llm.Tensor {
+	oldnew := []string{
+		"model.layers", "blk",
+		"w1", "ffn_gate_exps",
+		"w2", "ffn_down_exps",
+		"w3", "ffn_up_exps",
+	}
+
+	for i := range p.NumLocalExperts {
+		oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
+	}
+
+	// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
+	namer := strings.NewReplacer(oldnew...)
+	experts := make(map[string]experts)
+
+	// merge experts into a single tensor while removing them from ts
+	ts = slices.DeleteFunc(ts, func(t Tensor) bool {
+		if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
+			return false
+		}
+
+		name := namer.Replace(t.Name())
+		experts[name] = append(experts[name], t)
+		return true
+	})
+
+	var out []llm.Tensor
+	for n, e := range experts {
+		// TODO(mxyng): sanity check experts
+		out = append(out, llm.Tensor{
+			Name:     n,
+			Kind:     e[0].Kind(),
+			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),
+			WriterTo: e,
+		})
+	}
+
+	return append(out, p.llama.Tensors(ts)...)
+}
+
+type experts []Tensor
+
+func (e experts) WriteTo(w io.Writer) (int64, error) {
+	// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
+	for _, t := range e {
+		// the canonical merged experts tensor stacks all experts along a new, 0 axis,
+		// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
+		// this accomplishes the same thing by writing each expert tensor in sequence
+		if _, err := t.WriteTo(w); err != nil {
+			return 0, err
+		}
+	}
+
+	return 0, nil
+}

convert/convert_test.go

@@ -0,0 +1,126 @@
+package convert
+
+import (
+	"crypto/sha256"
+	"encoding/json"
+	"flag"
+	"fmt"
+	"io"
+	"io/fs"
+	"log/slog"
+	"math"
+	"os"
+	"path/filepath"
+	"slices"
+	"testing"
+
+	"github.com/ollama/ollama/llm"
+	"golang.org/x/exp/maps"
+)
+
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, llm.Tensors) {
+	t.Helper()
+
+	f, err := os.CreateTemp(t.TempDir(), "f16")
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer f.Close()
+
+	if err := Convert(fsys, f); err != nil {
+		t.Fatal(err)
+	}
+
+	r, err := os.Open(f.Name())
+	if err != nil {
+		t.Fatal(err)
+	}
+	t.Cleanup(func() { r.Close() })
+
+	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if _, err := r.Seek(0, io.SeekStart); err != nil {
+		t.Fatal(err)
+	}
+
+	return r, m.KV(), m.Tensors()
+}
+
+func TestMain(m *testing.M) {
+	var level slog.Level
+	flag.TextVar(&level, "level", slog.LevelInfo, "log level")
+	flag.Parse()
+	slog.SetLogLoggerLevel(level)
+	os.Exit(m.Run())
+}
+
+func TestConvertFull(t *testing.T) {
+	cases := []string{
+		"Meta-Llama-3-8B-Instruct",
+		"Mistral-7B-Instruct-v0.2",
+		"Mixtral-8x7B-Instruct-v0.1",
+		"gemma-2b-it",
+	}
+
+	for i := range cases {
+		tt := cases[i]
+		t.Run(tt, func(t *testing.T) {
+			t.Parallel()
+
+			p := filepath.Join("testdata", tt)
+			if testing.Short() {
+				t.Skip("skipping in short mode")
+			} else if _, err := os.Stat(p); err != nil {
+				t.Skipf("%s not found", p)
+			}
+
+			f, kv, tensors := convertFull(t, os.DirFS(p))
+			actual := make(map[string]string)
+			for k, v := range kv {
+				if s, ok := v.(json.Marshaler); !ok {
+					actual[k] = fmt.Sprintf("%v", v)
+				} else {
+					bts, err := json.Marshal(s)
+					if err != nil {
+						t.Fatal(err)
+					}
+
+					actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
+				}
+			}
+
+			for _, tensor := range tensors.Items {
+				sha256sum := sha256.New()
+				sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
+				if _, err := io.Copy(sha256sum, sr); err != nil {
+					t.Fatal(err)
+				}
+
+				actual[tensor.Name] = fmt.Sprintf("%x", sha256sum.Sum(nil))
+			}
+
+			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			var expect map[string]string
+			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
+				t.Fatal(err)
+			}
+
+			keys := maps.Keys(expect)
+			slices.Sort(keys)
+			for _, k := range keys {
+				if v, ok := actual[k]; !ok {
+					t.Errorf("missing %s", k)
+				} else if v != expect[k] {
+					t.Errorf("unexpected %s: want %s, got %s", k, expect[k], v)
+				}
+			}
+		})
+	}
+}

convert/fs.go

@@ -0,0 +1,58 @@
+package convert
+
+import (
+	"archive/zip"
+	"errors"
+	"io"
+	"io/fs"
+	"os"
+	"path/filepath"
+)
+
+type ZipReader struct {
+	r     *zip.Reader
+	p     string
+
+	// limit is the maximum size of a file that can be read directly
+	// from the zip archive. Files larger than this size will be extracted
+	limit int64
+}
+
+func NewZipReader(r *zip.Reader, p string, limit int64) fs.FS {
+	return &ZipReader{r, p, limit}
+}
+
+func (z *ZipReader) Open(name string) (fs.File, error) {
+	r, err := z.r.Open(name)
+	if err != nil {
+		return nil, err
+	}
+	defer r.Close()
+
+	if fi, err := r.Stat(); err != nil {
+		return nil, err
+	} else if fi.Size() < z.limit {
+		return r, nil
+	}
+
+	if !filepath.IsLocal(name) {
+		return nil, zip.ErrInsecurePath
+	}
+
+	n := filepath.Join(z.p, name)
+	if _, err := os.Stat(n); errors.Is(err, os.ErrNotExist) {
+		w, err := os.Create(n)
+		if err != nil {
+			return nil, err
+		}
+		defer w.Close()
+
+		if _, err := io.Copy(w, r); err != nil {
+			return nil, err
+		}
+	} else if err != nil {
+		return nil, err
+	}
+
+	return os.Open(n)
+}

convert/gemma.go

@@ -1,126 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"fmt"
-	"io"
-	"log/slog"
-	"os"
-	"strings"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type GemmaModel struct {
-	ModelData
-}
-
-func gemmaLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error {
-	slog.Debug(fmt.Sprintf("converting '%s'", r.t.Name))
-
-	data := make([]byte, r.end-r.start)
-	if err := binary.Read(f, r.bo, data); err != nil {
-		return err
-	}
-
-	tDataF32 := bfloat16.DecodeFloat32(data)
-
-	var err error
-	tDataF32, err = addOnes(tDataF32, int(r.t.Shape[0]))
-	if err != nil {
-		return err
-	}
-
-	if err := binary.Write(w, r.bo, tDataF32); err != nil {
-		return err
-	}
-	return nil
-}
-
-func addOnes(data []float32, vectorSize int) ([]float32, error) {
-	n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
-	ones := tensor.Ones(tensor.Float32, vectorSize)
-
-	var err error
-	n, err = n.Add(ones)
-	if err != nil {
-		return []float32{}, err
-	}
-
-	newN, err := native.SelectF32(n, 0)
-	if err != nil {
-		return []float32{}, err
-	}
-
-	var fullTensor []float32
-	for _, v := range newN {
-		fullTensor = append(fullTensor, v...)
-	}
-
-	return fullTensor, nil
-}
-
-func (m *GemmaModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
-
-	m.Tensors = []llm.Tensor{}
-	for _, l := range t {
-		if strings.HasSuffix(l.Name, "norm.weight") {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.handler = gemmaLayerHandler
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *GemmaModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *GemmaModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":                   "gemma",
-		"general.name":                           m.Name,
-		"gemma.context_length":                   uint32(m.Params.ContextSize),
-		"gemma.embedding_length":                 uint32(m.Params.HiddenSize),
-		"gemma.block_count":                      uint32(m.Params.HiddenLayers),
-		"gemma.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"gemma.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"gemma.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"gemma.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"gemma.attention.key_length":             uint32(m.Params.HeadDimension),
-		"gemma.attention.value_length":           uint32(m.Params.HeadDimension),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.padding_token_id": uint32(m.Params.PaddingTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(3),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}

convert/llama.go

@@ -1,162 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"fmt"
-	"io"
-	"log/slog"
-	"regexp"
-	"strings"
-
-	"github.com/nlpodyssey/gopickle/pytorch"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type LlamaModel struct {
-	ModelData
-}
-
-func llamaLayerHandler(w io.Writer, r torchWriterTo) error {
-	slog.Debug(fmt.Sprintf("repacking layer '%s'", r.t.Name))
-
-	data := r.storage.(*pytorch.HalfStorage).Data
-	tData := make([]uint16, len(data))
-	for cnt, v := range data {
-		tData[cnt] = uint16(float16.Fromfloat32(v))
-	}
-
-	var err error
-	var heads uint32
-	if strings.Contains(r.t.Name, "attn_q") {
-		heads = uint32(r.params.AttentionHeads)
-	} else if strings.Contains(r.t.Name, "attn_k") {
-		heads = uint32(r.params.KeyValHeads)
-		if heads == 0 {
-			heads = uint32(r.params.AttentionHeads)
-		}
-	} else {
-		return fmt.Errorf("unknown layer type")
-	}
-
-	slog.Debug(fmt.Sprintf("heads = %d", heads))
-
-	tData, err = llamaRepack(tData, int(heads), r.t.Shape)
-	if err != nil {
-		return err
-	}
-
-	if err = binary.Write(w, r.bo, tData); err != nil {
-		return err
-	}
-	return nil
-}
-
-func llamaRepack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
-	n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data))
-	origShape := n.Shape().Clone()
-
-	// reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf
-	if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(origShape...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-	newN, err := native.SelectU16(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var fullTensor []uint16
-	for _, v := range newN {
-		fullTensor = append(fullTensor, v...)
-	}
-	return fullTensor, nil
-}
-
-func (m *LlamaModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	m.Tensors = []llm.Tensor{}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			slog.Debug(fmt.Sprintf("setting handler for: %s", l.Name))
-			wt := l.WriterTo.(torchWriterTo)
-			wt.handler = llamaLayerHandler
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *LlamaModel) LoadVocab() error {
-	var v *Vocab
-	var err error
-
-	slog.Debug("loading vocab")
-	v, err = LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	slog.Debug("vocab loaded")
-
-	m.Vocab = v
-	return nil
-}
-
-func (m *LlamaModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":                   "llama",
-		"general.name":                           m.Name,
-		"llama.vocab_size":                       uint32(len(m.Vocab.Tokens)),
-		"llama.context_length":                   uint32(m.Params.ContextSize),
-		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
-		"llama.block_count":                      uint32(m.Params.HiddenLayers),
-		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}

convert/mistral.go

@@ -1,162 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"fmt"
-	"io"
-	"os"
-	"regexp"
-	"strings"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type MistralModel struct {
-	ModelData
-}
-
-func mistralLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error {
-	layerSize := r.end - r.start
-
-	var err error
-	tData := make([]uint16, layerSize/2)
-	if err = binary.Read(f, r.bo, tData); err != nil {
-		return err
-	}
-
-	var heads uint32
-	if strings.Contains(r.t.Name, "attn_q") {
-		heads = uint32(r.params.AttentionHeads)
-	} else if strings.Contains(r.t.Name, "attn_k") {
-		heads = uint32(r.params.KeyValHeads)
-		if heads == 0 {
-			heads = uint32(r.params.AttentionHeads)
-		}
-	} else {
-		return fmt.Errorf("unknown layer type")
-	}
-
-	tData, err = repack(tData, int(heads), r.t.Shape)
-	if err != nil {
-		return err
-	}
-
-	var buf []byte
-	for _, n := range tData {
-		buf = r.bo.AppendUint16(buf, n)
-	}
-
-	tempBuf := make([]uint16, len(tData))
-	tDataF32 := bfloat16.DecodeFloat32(buf)
-	for cnt, v := range tDataF32 {
-		tDataF16 := float16.Fromfloat32(v)
-		tempBuf[cnt] = uint16(tDataF16)
-	}
-
-	if err = binary.Write(w, r.bo, tempBuf); err != nil {
-		return err
-	}
-	return nil
-}
-
-func repack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
-	n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data))
-	origShape := n.Shape().Clone()
-
-	// reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf
-	if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(origShape...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-	newN, err := native.SelectU16(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var fullTensor []uint16
-	for _, v := range newN {
-		fullTensor = append(fullTensor, v...)
-	}
-	return fullTensor, nil
-}
-
-func (m *MistralModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	m.Tensors = []llm.Tensor{}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.handler = mistralLayerHandler
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *MistralModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":                   "llama",
-		"general.name":                           m.Name,
-		"llama.context_length":                   uint32(m.Params.ContextSize),
-		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
-		"llama.block_count":                      uint32(m.Params.HiddenLayers),
-		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}

convert/mixtral.go

@@ -1,85 +0,0 @@
-package convert
-
-import (
-	"io"
-	"regexp"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type MixtralModel struct {
-	ModelData
-}
-
-func (m *MixtralModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	m.Tensors = []llm.Tensor{}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.handler = mistralLayerHandler
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *MixtralModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *MixtralModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":          "llama",
-		"general.name":                  m.Name,
-		"llama.block_count":             uint32(m.Params.HiddenLayers),
-		"llama.context_length":          uint32(m.Params.ContextSize),
-		"llama.embedding_length":        uint32(m.Params.HiddenSize),
-		"llama.feed_forward_length":     uint32(m.Params.IntermediateSize),
-		"llama.attention.head_count":    uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
-
-		"llama.rope.freq_base":                   float32(m.Params.RopeFrequencyBase),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-
-		"llama.expert_count":      uint32(m.Params.Experts),
-		"llama.expert_used_count": uint32(m.Params.ExpertsUsed),
-
-		"llama.vocab_size":           uint32(len(m.Vocab.Tokens)),
-		"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-
-		"general.file_type":    uint32(1),
-		"tokenizer.ggml.model": "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}

convert/reader.go

@@ -0,0 +1,82 @@
+package convert
+
+import (
+	"errors"
+	"io"
+	"io/fs"
+	"strings"
+)
+
+type Tensor interface {
+	Name() string
+	Shape() []uint64
+	Kind() uint32
+	SetRepacker(repacker)
+	WriteTo(io.Writer) (int64, error)
+}
+
+type tensorBase struct {
+	name  string
+	shape []uint64
+	repacker
+}
+
+func (t tensorBase) Name() string {
+	return t.name
+}
+
+func (t tensorBase) Shape() []uint64 {
+	return t.shape
+}
+
+const (
+	tensorKindF32 uint32 = iota
+	tensorKindF16
+)
+
+func (t tensorBase) Kind() uint32 {
+	if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
+		return 0
+	}
+
+	switch len(t.shape) {
+	case 0:
+		panic("invalid tensor shape")
+	case 1:
+		return tensorKindF32
+	default:
+		return tensorKindF16
+	}
+}
+
+func (t *tensorBase) SetRepacker(fn repacker) {
+	t.repacker = fn
+}
+
+type repacker func(string, []float32, []uint64) ([]float32, error)
+
+func parseTensors(fsys fs.FS) ([]Tensor, error) {
+	patterns := []struct {
+		Pattern string
+		Func    func(fs.FS, ...string) ([]Tensor, error)
+	}{
+		{"model-*-of-*.safetensors", parseSafetensors},
+		{"model.safetensors", parseSafetensors},
+		{"pytorch_model-*-of-*.bin", parseTorch},
+		{"pytorch_model.bin", parseTorch},
+		{"consolidated.*.pth", parseTorch},
+	}
+
+	for _, pattern := range patterns {
+		matches, err := fs.Glob(fsys, pattern.Pattern)
+		if err != nil {
+			return nil, err
+		}
+
+		if len(matches) > 0 {
+			return pattern.Func(fsys, matches...)
+		}
+	}
+
+	return nil, errors.New("unknown tensor format")
+}

convert/reader_safetensors.go

@@ -0,0 +1,149 @@
+package convert
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"io/fs"
+	"slices"
+
+	"github.com/d4l3k/go-bfloat16"
+	"github.com/x448/float16"
+	"golang.org/x/exp/maps"
+)
+
+type safetensorMetadata struct {
+	Type    string   `json:"dtype"`
+	Shape   []uint64 `json:"shape"`
+	Offsets []int64  `json:"data_offsets"`
+}
+
+func parseSafetensors(fsys fs.FS, ps ...string) ([]Tensor, error) {
+	var ts []Tensor
+	for _, p := range ps {
+		f, err := fsys.Open(p)
+		if err != nil {
+			return nil, err
+		}
+		defer f.Close()
+
+		var n int64
+		if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
+			return nil, err
+		}
+
+		b := bytes.NewBuffer(make([]byte, 0, n))
+		if _, err = io.CopyN(b, f, n); err != nil {
+			return nil, err
+		}
+
+		var headers map[string]safetensorMetadata
+		if err := json.NewDecoder(b).Decode(&headers); err != nil {
+			return nil, err
+		}
+
+		keys := maps.Keys(headers)
+		slices.Sort(keys)
+
+		for _, key := range keys {
+			if value := headers[key]; value.Type != "" {
+				ts = append(ts, safetensor{
+					fs:     fsys,
+					path:   p,
+					dtype:  value.Type,
+					offset: safetensorsPad(n, value.Offsets[0]),
+					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
+					tensorBase: &tensorBase{
+						name:  key,
+						shape: value.Shape,
+					},
+				})
+			}
+		}
+	}
+
+	return ts, nil
+}
+
+// safetensorsPad returns the padded size of the safetensors file given a length n and offset s
+func safetensorsPad(n, offset int64) int64 {
+	return 8 + n + offset
+}
+
+type safetensor struct {
+	fs     fs.FS
+	path   string
+	dtype  string
+	offset int64
+	size   int64
+	*tensorBase
+}
+
+func (st safetensor) WriteTo(w io.Writer) (int64, error) {
+	f, err := st.fs.Open(st.path)
+	if err != nil {
+		return 0, err
+	}
+	defer f.Close()
+
+	if seeker, ok := f.(io.Seeker); ok {
+		if _, err := seeker.Seek(st.offset, io.SeekStart); err != nil {
+			return 0, err
+		}
+	} else {
+		if _, err := io.CopyN(io.Discard, f, st.offset); err != nil {
+			return 0, err
+		}
+	}
+
+	var f32s []float32
+	switch st.dtype {
+	case "F32":
+		f32s = make([]float32, st.size/4)
+		if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
+			return 0, err
+		}
+	case "F16":
+		u16s := make([]uint16, st.size/2)
+		if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
+			return 0, err
+		}
+
+		for _, b := range u16s {
+			f32s = append(f32s, float16.Frombits(b).Float32())
+		}
+
+	case "BF16":
+		u8s := make([]uint8, st.size)
+		if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
+			return 0, err
+		}
+
+		f32s = bfloat16.DecodeFloat32(u8s)
+	default:
+		return 0, fmt.Errorf("unknown data type: %s", st.dtype)
+	}
+
+	if st.repacker != nil {
+		f32s, err = st.repacker(st.Name(), f32s, st.Shape())
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch st.Kind() {
+	case tensorKindF32:
+		return 0, binary.Write(w, binary.LittleEndian, f32s)
+	case tensorKindF16:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, binary.LittleEndian, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
+	}
+}

convert/reader_torch.go

@@ -0,0 +1,47 @@
+package convert
+
+import (
+	"io"
+	"io/fs"
+
+	"github.com/nlpodyssey/gopickle/pytorch"
+	"github.com/nlpodyssey/gopickle/types"
+)
+
+func parseTorch(fsys fs.FS, ps ...string) ([]Tensor, error) {
+	var ts []Tensor
+	for _, p := range ps {
+		pt, err := pytorch.Load(p)
+		if err != nil {
+			return nil, err
+		}
+
+		for _, k := range pt.(*types.Dict).Keys() {
+			t := pt.(*types.Dict).MustGet(k)
+
+			var shape []uint64
+			for dim := range t.(*pytorch.Tensor).Size {
+				shape = append(shape, uint64(dim))
+			}
+
+			ts = append(ts, torch{
+				storage: t.(*pytorch.Tensor).Source,
+				tensorBase: &tensorBase{
+					name:  k.(string),
+					shape: shape,
+				},
+			})
+		}
+	}
+
+	return ts, nil
+}
+
+type torch struct {
+	storage pytorch.StorageInterface
+	*tensorBase
+}
+
+func (pt torch) WriteTo(w io.Writer) (int64, error) {
+	return 0, nil
+}

convert/safetensors.go

@@ -1,317 +0,0 @@
-package convert
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"log/slog"
-	"os"
-	"path/filepath"
-	"regexp"
-	"slices"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/mitchellh/mapstructure"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type safetensorWriterTo struct {
-	t *llm.Tensor
-
-	params *Params
-	bo     ByteOrder
-
-	filename string
-
-	start, end, padding uint64
-	handler             func(w io.Writer, r safetensorWriterTo, f *os.File) error
-}
-
-type tensorMetaData struct {
-	Type    string `mapstructure:"dtype"`
-	Shape   []int  `mapstructure:"shape"`
-	Offsets []int  `mapstructure:"data_offsets"`
-}
-
-type SafetensorFormat struct{}
-
-func (m *SafetensorFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
-	slog.Debug("getting tensor data")
-	var tensors []llm.Tensor
-	files, err := filepath.Glob(filepath.Join(dirpath, "/model-*.safetensors"))
-	if err != nil {
-		return nil, err
-	}
-
-	var offset uint64
-	for _, f := range files {
-		var t []llm.Tensor
-		var err error
-		t, offset, err = m.readTensors(f, offset, params)
-		if err != nil {
-			slog.Error(err.Error())
-			return nil, err
-		}
-		tensors = append(tensors, t...)
-	}
-	slog.Debug(fmt.Sprintf("all tensors = %d", len(tensors)))
-	return tensors, nil
-}
-
-func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
-	f, err := os.Open(fn)
-	if err != nil {
-		return nil, 0, err
-	}
-	defer f.Close()
-
-	var jsonSize uint64
-	if err := binary.Read(f, binary.LittleEndian, &jsonSize); err != nil {
-		return nil, 0, err
-	}
-
-	buf := make([]byte, jsonSize)
-	_, err = io.ReadFull(f, buf)
-	if err != nil {
-		return nil, 0, err
-	}
-
-	d := json.NewDecoder(bytes.NewBuffer(buf))
-	d.UseNumber()
-	var parsed map[string]interface{}
-	if err = d.Decode(&parsed); err != nil {
-		return nil, 0, err
-	}
-
-	var keys []string
-	for k := range parsed {
-		keys = append(keys, k)
-	}
-
-	slices.Sort(keys)
-	slog.Info("converting layers")
-
-	var tensors []llm.Tensor
-	for _, k := range keys {
-		vals := parsed[k].(map[string]interface{})
-		var data tensorMetaData
-		if err = mapstructure.Decode(vals, &data); err != nil {
-			slog.Error("couldn't decode properly")
-			return nil, 0, err
-		}
-
-		var size uint64
-		var kind uint32
-		switch len(data.Shape) {
-		case 0:
-			// metadata
-			continue
-		case 1:
-			// convert to float32
-			kind = 0
-			size = uint64(data.Shape[0] * 4)
-		case 2:
-			// convert to float16
-			kind = 1
-			size = uint64(data.Shape[0] * data.Shape[1] * 2)
-		}
-
-		ggufName, err := m.GetLayerName(k)
-		if err != nil {
-			slog.Error(err.Error())
-			return nil, 0, err
-		}
-
-		shape := []uint64{0, 0, 0, 0}
-		for i := range data.Shape {
-			shape[i] = uint64(data.Shape[i])
-		}
-
-		t := llm.Tensor{
-			Name:   ggufName,
-			Kind:   kind,
-			Offset: offset,
-			Shape:  shape[:],
-		}
-
-		t.WriterTo = safetensorWriterTo{
-			t:        &t,
-			params:   params,
-			bo:       params.ByteOrder,
-			filename: fn,
-			start:    uint64(data.Offsets[0]),
-			end:      uint64(data.Offsets[1]),
-			padding:  8 + jsonSize,
-		}
-
-		offset += size
-		tensors = append(tensors, t)
-	}
-
-	slog.Debug(fmt.Sprintf("total tensors for file = %d", len(tensors)))
-	slog.Debug(fmt.Sprintf("offset = %d", offset))
-
-	return tensors, offset, nil
-}
-
-func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
-	f, err := os.Open(filepath.Join(dirpath, "config.json"))
-	if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	var params Params
-
-	d := json.NewDecoder(f)
-	err = d.Decode(&params)
-	if err != nil {
-		return nil, err
-	}
-
-	params.ByteOrder = binary.LittleEndian
-	return &params, nil
-}
-
-func (m *SafetensorFormat) GetLayerName(n string) (string, error) {
-	directMap := map[string]string{
-		"model.embed_tokens.weight": "token_embd.weight",
-		"lm_head.weight":            "output.weight",
-		"model.norm.weight":         "output_norm.weight",
-	}
-
-	tMap := map[string]string{
-		"model.layers.(\\d+).input_layernorm.weight":                    "blk.$1.attn_norm.weight",
-		"model.layers.(\\d+).mlp.down_proj.weight":                      "blk.$1.ffn_down.weight",
-		"model.layers.(\\d+).mlp.gate_proj.weight":                      "blk.$1.ffn_gate.weight",
-		"model.layers.(\\d+).mlp.up_proj.weight":                        "blk.$1.ffn_up.weight",
-		"model.layers.(\\d+).post_attention_layernorm.weight":           "blk.$1.ffn_norm.weight",
-		"model.layers.(\\d+).self_attn.k_proj.weight":                   "blk.$1.attn_k.weight",
-		"model.layers.(\\d+).self_attn.o_proj.weight":                   "blk.$1.attn_output.weight",
-		"model.layers.(\\d+).self_attn.q_proj.weight":                   "blk.$1.attn_q.weight",
-		"model.layers.(\\d+).self_attn.v_proj.weight":                   "blk.$1.attn_v.weight",
-		"model.layers.(\\d+).block_sparse_moe.gate.weight":              "blk.$1.ffn_gate_inp.weight",
-		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w1.weight": "blk.$1.ffn_gate.$2.weight",
-		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w2.weight": "blk.$1.ffn_down.$2.weight",
-		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w3.weight": "blk.$1.ffn_up.$2.weight",
-	}
-
-	v, ok := directMap[n]
-	if ok {
-		return v, nil
-	}
-
-	// quick hack to rename the layers to gguf format
-	for k, v := range tMap {
-		re := regexp.MustCompile(k)
-		newName := re.ReplaceAllString(n, v)
-		if newName != n {
-			return newName, nil
-		}
-	}
-
-	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
-}
-
-func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
-	f, err := os.Open(r.filename)
-	if err != nil {
-		return 0, err
-	}
-	defer f.Close()
-
-	if _, err = f.Seek(int64(r.padding+r.start), 0); err != nil {
-		return 0, err
-	}
-
-	// use the handler if one is present
-	if r.handler != nil {
-		return 0, r.handler(w, r, f)
-	}
-
-	remaining := r.end - r.start
-
-	bufSize := uint64(10240)
-	var finished bool
-	for {
-		data := make([]byte, min(bufSize, remaining))
-
-		b, err := io.ReadFull(f, data)
-		remaining -= uint64(b)
-
-		if err == io.EOF || remaining <= 0 {
-			finished = true
-		} else if err != nil {
-			return 0, err
-		}
-
-		// convert bfloat16 -> ieee float32
-		tDataF32 := bfloat16.DecodeFloat32(data)
-
-		switch r.t.Kind {
-		case 0:
-			if err := binary.Write(w, r.bo, tDataF32); err != nil {
-				return 0, err
-			}
-		case 1:
-			// convert float32 -> float16
-			tempBuf := make([]uint16, len(data)/2)
-			for cnt, v := range tDataF32 {
-				tDataF16 := float16.Fromfloat32(v)
-				tempBuf[cnt] = uint16(tDataF16)
-			}
-			if err := binary.Write(w, r.bo, tempBuf); err != nil {
-				return 0, err
-			}
-		}
-		if finished {
-			break
-		}
-	}
-	return 0, nil
-}
-
-func (m *SafetensorFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
-	switch len(params.Architectures) {
-	case 0:
-		return nil, fmt.Errorf("No architecture specified to convert")
-	case 1:
-		switch params.Architectures[0] {
-		case "MistralForCausalLM":
-			return &MistralModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		case "MixtralForCausalLM":
-			return &MixtralModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		case "GemmaForCausalLM":
-			return &GemmaModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		default:
-			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
-		}
-	}
-
-	return nil, fmt.Errorf("Unknown error")
-}

convert/testdata/Meta-Llama-3-8B-Instruct.json

@@ -0,0 +1,313 @@
+{
+  "general.architecture": "llama",
+  "general.file_type": "1",
+  "general.quantization_version": "2",
+  "llama.block_count": "32",
+  "llama.context_length": "8192",
+  "llama.embedding_length": "4096",
+  "llama.feed_forward_length": "14336",
+  "llama.rope.dimension_count": "128",
+  "llama.rope.freq_base": "500000",
+  "llama.vocab_size": "128256",
+  "llama.attention.head_count": "32",
+  "llama.attention.head_count_kv": "8",
+  "llama.attention.layer_norm_rms_epsilon": "1e-05",
+  "tokenizer.ggml.model": "gpt2",
+  "tokenizer.ggml.pre": "llama-bpe",
+  "tokenizer.ggml.bos_token_id": "128000",
+  "tokenizer.ggml.eos_token_id": "128009",
+  "tokenizer.ggml.merges": "d0cbac1fcc9dcf03724b8db5c9bfb593ae1cf68fb9bc72eb1d15274dcbbf618b",
+  "tokenizer.ggml.token_type": "d70a88809fd7da6f1f028622685cd64268a7a922c5d343c96f25b66327358978",
+  "tokenizer.ggml.tokens": "765b529dbcbc42dd202ce657341c63807b51f3b07e09898f6aa6196326865d5a",
+  "token_embd.weight": "b53102a11d9064bbd404833e3464b1b13e08ce73300b442312cccde2f19b2698",
+  "blk.0.attn_norm.weight": "7318df3cca9e8d153ff0a503026a1265e63d20b2a8c1dd7a2769585082b5d1ee",
+  "blk.0.ffn_down.weight": "b950806a1fc722c9fad7fd0b20c3c0a7fb50f14395e1e7663a590bfd62e20900",
+  "blk.0.ffn_gate.weight": "e73e580af6d4f08e060a74a3c25efdf5d3bed99e183d95a5a85ae859014839fd",
+  "blk.0.ffn_up.weight": "c8158af679ef99746da1befb67eebb19489e0bbe6ce7d97e13e348508244e516",
+  "blk.0.ffn_norm.weight": "7ec69c3c31e95e49a3359003b0033f6b9e85561a3e3fd83e7476661ecdd756bb",
+  "blk.0.attn_k.weight": "2732303257bac969b4964e0e32ec08b5a7f5c031bb02bf6ac4467b3ea0ebcf1e",
+  "blk.0.attn_output.weight": "ecda1d43b4ccc91cd5b366d7e7a275353990ac78561a07c83d9c77031aba12dc",
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+}

convert/testdata/Mistral-7B-Instruct-v0.2.json

@@ -0,0 +1,313 @@
+{
+  "general.architecture": "llama",
+  "general.file_type": "1",
+  "general.quantization_version": "2",
+  "llama.block_count": "32",
+  "llama.context_length": "32768",
+  "llama.embedding_length": "4096",
+  "llama.feed_forward_length": "14336",
+  "llama.attention.head_count": "32",
+  "llama.attention.head_count_kv": "8",
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+}

convert/testdata/Mixtral-8x7B-Instruct-v0.1.json

@@ -0,0 +1,348 @@
+{
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+  "general.file_type": "1",
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+}

convert/testdata/gemma-2b-it.json

@@ -0,0 +1,188 @@
+{
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+  "blk.8.attn_norm.weight": "75b850c481a69083ae09d0207ba7317b37c735a39fcf5fef5400e6c84fb1257f",
+  "blk.8.attn_output.weight": "cbd669dbdea2bdd90f9f0cc97566b3dffff3c56cecb4f47290ceef30da83b2d6",
+  "blk.8.attn_q.weight": "9edcb63087a431bac361822497e6ecdaa06d9ea4a1a754e36da7ba9f8db81c7c",
+  "blk.8.attn_v.weight": "3fb72c2c4f95a83626aa3e30062f9450b09ab37c7871e229f18bbc5cf744633c",
+  "blk.8.ffn_down.weight": "bd69d2c9172974fff154441b237b4787fb53b2d185325442d5048130ef5bc4ef",
+  "blk.8.ffn_gate.weight": "d04689c80553edd011d1cbaa5d570fffa7fa91e88b66cf1352d89ab60b72f908",
+  "blk.8.ffn_norm.weight": "e49984183b735b7f2c4e4730c289eed9394056d2e283a00fd83ea0915df31a73",
+  "blk.8.ffn_up.weight": "8fe62a1ce8e847e567add6c6f6bf2922bc467495b5eb4c116b3cb85b85b3b211",
+  "blk.9.attn_k.weight": "d90904959e5004cf0d6e729c6bff18cc33c094798b802473c1ec55ab8d276183",
+  "blk.9.attn_norm.weight": "79277f290cc07411115d8fa138045edf4a17b3416ab2145409cbe8ab829fd4ee",
+  "blk.9.attn_output.weight": "5a21bf2e1f09a81405025f96d4153ffb630158e17269cff8ffff935c38ceb1a7",
+  "blk.9.attn_q.weight": "51b1d0febc3b350945be4504f55afa4347517bde0f710e1a4b88e6b17e71e7c7",
+  "blk.9.attn_v.weight": "aab7e1db0a8b50a03036356791ffce736ab010d15674c96eaef8049d80076054",
+  "blk.9.ffn_down.weight": "cbf43ec84becb40c9359a181ab0e641fd7faae7d34b549501f7cfb7afdc3d764",
+  "blk.9.ffn_gate.weight": "dce0e8661c778327bed7f03b6790d26710764188aed9dc746e6e05863891fa57",
+  "blk.9.ffn_norm.weight": "6d41642104f995c77bf31122b13237caebda3e7fcccb1367ce91db36b015e923",
+  "blk.9.ffn_up.weight": "82fe4c67bf24e7b2d6f6e05f7b1234c2bf90c3932951091a9066211b8e15ecbb",
+  "blk.10.attn_k.weight": "f6a9ed8fd8d3229b5d03175c413ffc56a07f2ce7236271986361dd3d8993f9aa",
+  "blk.10.attn_norm.weight": "cebbef89f0326ca8e02df3867a571e4d61c20c2a12f295f98ae590d62bc86010",
+  "blk.10.attn_output.weight": "34f5efb86accb4f06347d83a32558ea8eab3039d128969161a741ebacbb656ff",
+  "blk.10.attn_q.weight": "1e0efe27df2d5d50f7157253ba2cfd436d6781c3dc78ca176d0c16a210b5b763",
+  "blk.10.attn_v.weight": "8f085bf50a2b0f83cd6cdda3c8ef5a9e204a36348ed95871aac725d1f68640cf",
+  "blk.10.ffn_down.weight": "bf3b3cb4cace435809ac7b4cc933f20853af12f1f272d3dcefe7f19c0f203b8b",
+  "blk.10.ffn_gate.weight": "d3df7a1413b1c5adf1a1dcda9e5225a15c89874bae53bb6137ad1ea42fca2d34",
+  "blk.10.ffn_norm.weight": "a1da603b0480471b5ed8e862148cecd5fed918f8304d6933ab0bdb25b8d2fb8f",
+  "blk.10.ffn_up.weight": "bffbba605922e972dc47dda88a0b4659aa52236c76e5fe861a949e6d9a367492",
+  "blk.11.attn_k.weight": "9f31c63d66cd32c29b1eb8bb829d0c8525ce2ae936e0eefdaab6335a2d12a3df",
+  "blk.11.attn_norm.weight": "0bde1a266d8b2e8f202bb7e2e88b19147ca83021901f6d3cae77a4df5548c754",
+  "blk.11.attn_output.weight": "e10725c7cf746ed4a7e472cf7aea6cb564e5db6a1d5197adc980d650a387ccea",
+  "blk.11.attn_q.weight": "05ee758a7d065802630f8c65dca424364c1c8825e389aa33f9405c45e8a50cce",
+  "blk.11.attn_v.weight": "0c3ae7090f11775d24c51120db6e305db6aff706493e7ee123dcab74485ba789",
+  "blk.11.ffn_down.weight": "7ba40b8e12c09c5fb2006b77a771cb01ce894e88a3b3e1877f927a5b89c91709",
+  "blk.11.ffn_gate.weight": "db76388a023b98097972d354ba1c6a5e26efdeb1c596b9c28bf2cd8f6596975e",
+  "blk.11.ffn_norm.weight": "a38c3ae1b89a68ddc7b72c99c5b28be7fe3787c4fad9904d0c43d64eaf00c474",
+  "blk.11.ffn_up.weight": "13c8142f9cf1eddc658babf978daf3515c4ccc45f849f3e7e3930aa18a8480a0",
+  "blk.12.attn_k.weight": "f03241c36ac87cb57429a2ef22186b8d7d0b590a8b173beb01fa13d93772f3b1",
+  "blk.12.attn_norm.weight": "4568f654e6d65104d586e7c16ba960c83428698ce103022b7e0be15e2884e13b",
+  "blk.12.attn_output.weight": "04867603f82f91e41306e09b33ecda0104b3ee4834061f2c0bbdc8da33c72509",
+  "blk.12.attn_q.weight": "70fe04b9a8e08b6100cc8d6b58bf4cbbad15ca1de82d63baca5d352ba6c4cbae",
+  "blk.12.attn_v.weight": "15cb28db61a86c98687991d7e611bc92a1fcc6007f3432149cfb5fe518a4f65e",
+  "blk.12.ffn_down.weight": "6d10c790a4e3dc44c2dc36d96251ae97cdf30a4fa04d4c43e31bfbd038e6a7b7",
+  "blk.12.ffn_gate.weight": "3462a2d8f6b4743b25e24da51b90018ac2858d05ac7e582bcb69063cfdac1104",
+  "blk.12.ffn_norm.weight": "1f96392c1faa34e34ae5dea55a6a86c5aa4c79758952075d53d28de89dd88456",
+  "blk.12.ffn_up.weight": "d22eacc612a7411953d948483c5fb201e11722955ee0754da866e7bec578ac6d",
+  "blk.13.attn_k.weight": "5864977e6b733ea942647d6feed5c76156c48c200649c22e4e11b9e5860e57f3",
+  "blk.13.attn_norm.weight": "87e053535144723db4145aa5402acc54331b7696752d852bb9fc542ff33f0fb5",
+  "blk.13.attn_output.weight": "078145f5ad83f8b14f97a869346f7fd1583b24d1e3edadaa95d3da4242973f8f",
+  "blk.13.attn_q.weight": "3b8caf35504cbc4d1a7dd6e011a95760703b7f71e2218b030b1254f811362dd7",
+  "blk.13.attn_v.weight": "4fdf8365a603e043e5b40c4a21c84ac167f9be62794178f9d8a608dfe5653bf9",
+  "blk.13.ffn_down.weight": "a07d3abbfcacf48ba028df2cab895be32cc15022d23389a745286e79c1b1d1fd",
+  "blk.13.ffn_gate.weight": "1d2ab39666aa2909acc96787432a3ed13b19d25170f74665fadff9b17bbaffb1",
+  "blk.13.ffn_norm.weight": "4f2e809fda5f3eadf52578ee50e0ba36e53be91e55dce418c12dfe595f5f18e7",
+  "blk.13.ffn_up.weight": "8783d2720c2c37ca176a5801e0b3ef1f9cc9cf3ef1cd37af423aaf6b2a27e2bd",
+  "blk.14.attn_k.weight": "ce9428e2b55d43ae0c6690dbd56182f99adc427694ba8236b405cc8ea5035e86",
+  "blk.14.attn_norm.weight": "6abb35f9db8251d6ae954bda147c6ada2371b0574d11702e828f3c6ac99b7cc0",
+  "blk.14.attn_output.weight": "fe3880916d0ceb5bff672c88bbefb7060a545be609bf049beb2024b38221836d",
+  "blk.14.attn_q.weight": "7c8ad81be6f4a350931fd108b5f7c9e366e8c26ef62d1d85ffef5dca8fd893f8",
+  "blk.14.attn_v.weight": "e4bdedffacbebe38567a0734dfd67db90e911d9a9669fcde9a7c4ad8a0066c52",
+  "blk.14.ffn_down.weight": "ef6694dff1e05820aac0cd2b22f39ac7788b4967afc9250775575554c66aab2c",
+  "blk.14.ffn_gate.weight": "db63c4179e2db704bc505e2b4696e055b593e295a1b7c4c586fc793bdd5aab19",
+  "blk.14.ffn_norm.weight": "2796a62d832a9710148f95d533320492a33e712b2e5218659c548705bd11684d",
+  "blk.14.ffn_up.weight": "3f78c78d8c2d54df45f799d4ff902316628af296834afe4ceed63d4a324ff03e",
+  "blk.15.attn_k.weight": "6e810ee3859e07695645ee0c9a5efc7962668984a5f0a9325f47e462743b447c",
+  "blk.15.attn_norm.weight": "0956b576ae96db0b28cb09f761f801cfd9281432284664f0fe181c8d9c55d1ec",
+  "blk.15.attn_output.weight": "03a17f7e94208177aace5cc41b7f54670ba57873b7274ff6e23caf58cce110ca",
+  "blk.15.attn_q.weight": "b8edafe7d2216a6f8b4ae4905a906475490e6ea418f6e1d3cec563dbdc6fab91",
+  "blk.15.attn_v.weight": "f8ae8cae0f4cfa34a459824eba57350c3c248104ba5607e7d9dc7d7c39aaf4a6",
+  "blk.15.ffn_down.weight": "8d02eb439da852246d2ca67e9b7b6de0b090b80744355e64728a23e41926505b",
+  "blk.15.ffn_gate.weight": "ed5bf361c67db8731f186b775826f21c33bdb521111fd2d922539719a770239f",
+  "blk.15.ffn_norm.weight": "5942ca3c73209ac9a0c8bfd9b4aab7f7be7aee9aa12d9c35833493b44af76767",
+  "blk.15.ffn_up.weight": "f4bebf4ad99ec5f911327dec347be6c595814885309c7bc5647ce28c7f4d1cf5",
+  "blk.16.attn_k.weight": "756a534c19364448e0958b8948fe33891c6ccda0fbb4dfa2024e1f532a87804b",
+  "blk.16.attn_norm.weight": "386b7b9e4e6509f6af9c022d942b6c6c6cc136aeed8751ecb037c74d7c4bfb93",
+  "blk.16.attn_output.weight": "3ba1a766a25830b84d7c22178203635f9c5624caad290bc5e5d73da5d5e7a2ec",
+  "blk.16.attn_q.weight": "d39b0c91e1fda7685d50a0f7cc8d18c44b5bdc90a142c7fda0bc329cca1afa74",
+  "blk.16.attn_v.weight": "98b33fcb0ee3483cff1b06ecb44d7b7ffb4d34c268248e4d73dfdf82b2065b2f",
+  "blk.16.ffn_down.weight": "14006f5e4acb2f9416271ae562e299359cd2585739c7fc77ccbca54495563948",
+  "blk.16.ffn_gate.weight": "12f8abae2d301d8f88bedb6af98b1daecc7b0b8d05148594f931f30958d77aca",
+  "blk.16.ffn_norm.weight": "129a15a046ee96d06de288bd43c80f77a6b0fb3a159c7367154c6e4aaf362672",
+  "blk.16.ffn_up.weight": "b4a5911a45f3871ef1d4efb7dc7108645a564b70f818eccf45beebef2e844ee9",
+  "blk.17.attn_k.weight": "5e1bfcff0146ebdde3817b656952892eb671e14e75afc92fa53f84f8eecbec4c",
+  "blk.17.attn_norm.weight": "60bc988fab7c4b29ee9de599df41a8de00caa94fcd74677da011fac82f60f465",
+  "blk.17.attn_output.weight": "ba49b40d6a0b5685f749c24b0edbed3adc44dbe13b5d5e5fa1e56169fc746555",
+  "blk.17.attn_q.weight": "82bb415d24efcd14d03ace03f907bb70db6a204c76a0bdd1892e0fba165db87d",
+  "blk.17.attn_v.weight": "73dbe54beb91a899884e275ea81ffc5187a20cb7d5b68d5c299b783096999d94",
+  "blk.17.ffn_down.weight": "7c086166241e0664f8963fd1ca4ed74c737abfb2525ec20f8435821ff50158f3",
+  "blk.17.ffn_gate.weight": "51a32f78244d42a539f619c5ce661db9e6cf41636280a826d439b5444edcd28c",
+  "blk.17.ffn_norm.weight": "c4bb247fccd1ecc84875028af63dd20aaf5cbd17eb94a9bc36679c09285dccab",
+  "blk.17.ffn_up.weight": "b5886182790bc6fbadd63de9bc4ffee416f3b69a66280d197ab8c18edf769abf",
+  "output_norm.weight": "481f3097d0a20412e35b3a739b1b958487bcd41ff67744baa3c9acbddd2ee4d4"
+}

convert/tokenizer.go

@@ -0,0 +1,265 @@
+package convert
+
+import (
+	"cmp"
+	"crypto/sha256"
+	"encoding/hex"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"io/fs"
+	"log/slog"
+	"os"
+	"slices"
+)
+
+const (
+	_ int32 = iota
+	tokenTypeNormal
+	tokenTypeUnknown
+	tokenTypeControl
+	tokenTypeUserDefined
+	tokenTypeUnused
+	tokenTypeByte
+)
+
+type Tokenizer struct {
+	*Vocabulary
+	SpecialVocabulary []*SpecialVocabulary
+	Merges            []string
+
+	Pre      string
+	Template string
+}
+
+func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error) {
+	v, err := parseVocabulary(fsys)
+	if err != nil {
+		return nil, err
+	}
+
+	t := &Tokenizer{
+		Vocabulary: v,
+		Pre:        "default",
+	}
+
+	addedTokens := make(map[string]token)
+	if f, err := fsys.Open("tokenizer.json"); errors.Is(err, os.ErrNotExist) {
+	} else if err != nil {
+		return nil, err
+	} else {
+		defer f.Close()
+
+		var tt tokenizer
+		if err := json.NewDecoder(f).Decode(&tt); err != nil {
+			return nil, err
+		}
+
+		for _, t := range tt.AddedTokens {
+			addedTokens[t.Content] = t
+		}
+
+		t.Merges = tt.Model.Merges
+
+		sha256sum := sha256.New()
+		for _, pt := range tt.PreTokenizer.PreTokenizers {
+			switch pt.Type {
+			case "Split":
+				if pt.Pattern.Regex != "" {
+					// create a checksum of all Split pretokenizers which should be sufficient
+					// to identify the pretokenizer
+					sha256sum.Write([]byte(pt.Pattern.Regex))
+				}
+			}
+		}
+
+		switch digest := hex.EncodeToString(sha256sum.Sum(nil)); digest {
+		case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
+			t.Pre = "llama-bpe"
+		case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
+			t.Pre = "deepseek-llm"
+		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
+			t.Pre = "deepseek-coder"
+		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
+			// noop, empty pretokenizer
+		default:
+			slog.Warn("unknown pretokenizer, using default", "digest", digest)
+		}
+	}
+
+	if f, err := fsys.Open("tokenizer_config.json"); errors.Is(err, os.ErrNotExist) {
+	} else if err != nil {
+		return nil, err
+	} else {
+		defer f.Close()
+
+		var p map[string]json.RawMessage
+		if err := json.NewDecoder(f).Decode(&p); err != nil {
+			return nil, err
+		}
+
+		if template, ok := p["chat_template"]; ok {
+			if err := json.Unmarshal(template, &t.Template); err != nil {
+				return nil, err
+			}
+		}
+
+		for _, st := range specialTokenTypes {
+			sv := SpecialVocabulary{Type: st}
+			if bts, ok := p[fmt.Sprintf("add_%s_token", st)]; ok {
+				if err := json.Unmarshal(bts, &sv.AddToken); err != nil {
+					return nil, err
+				}
+			}
+
+			if bts, ok := p[fmt.Sprintf("%s_token", st)]; ok {
+				var content string
+				if err := json.Unmarshal(bts, &content); err != nil {
+					var mm map[string]any
+					if err := json.Unmarshal(bts, &mm); err != nil {
+						continue
+					}
+
+					content, ok = mm["content"].(string)
+					if !ok {
+						continue
+					}
+				}
+
+				sv.Content = content
+			}
+
+			if id, ok := addedTokens[sv.Content]; ok {
+				sv.ID = id.ID
+				t.SpecialVocabulary = append(t.SpecialVocabulary, &sv)
+			}
+		}
+	}
+
+	return t, nil
+}
+
+type tokenizer struct {
+	Version     string  `json:"version"`
+	AddedTokens []token `json:"added_tokens"`
+	Model       struct {
+		Type   string         `json:"type"`
+		Vocab  map[string]int `json:"vocab"`
+		Merges []string       `json:"merges"`
+	} `json:"model"`
+
+	PreTokenizer struct {
+		PreTokenizers []struct {
+			Type    string `json:"type"`
+			Pattern struct {
+				Regex string `json:"Regex"`
+			} `json:"pattern"`
+		} `json:"pretokenizers"`
+	} `json:"pre_tokenizer"`
+}
+
+type token struct {
+	ID          int    `json:"id"`
+	Content     string `json:"content"`
+	Special     bool   `json:"special"`
+	UserDefined bool
+}
+
+type Vocabulary struct {
+	Model  string
+	Tokens []string
+	Scores []float32
+	Types  []int32
+}
+
+func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
+	f, err := fsys.Open("tokenizer.json")
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var t tokenizer
+	if err := json.NewDecoder(f).Decode(&t); err != nil {
+		return nil, err
+	}
+
+	var tokens []token
+	for k, v := range t.Model.Vocab {
+		tokens = append(tokens, token{
+			ID:      v,
+			Content: k,
+		})
+	}
+
+	for _, t := range t.AddedTokens {
+		t.UserDefined = true
+		tokens = append(tokens, t)
+	}
+
+	slices.SortFunc(tokens, func(i, j token) int {
+		return cmp.Compare(i.ID, j.ID)
+	})
+
+	v := Vocabulary{Model: "gpt2"}
+	for _, t := range tokens {
+		v.Tokens = append(v.Tokens, t.Content)
+		v.Scores = append(v.Scores, float32(t.ID))
+
+		switch {
+		case t.Special:
+			v.Types = append(v.Types, tokenTypeControl)
+		case t.UserDefined:
+			v.Types = append(v.Types, tokenTypeUserDefined)
+		default:
+			v.Types = append(v.Types, tokenTypeNormal)
+		}
+	}
+
+	return &v, nil
+}
+
+func parseVocabulary(fsys fs.FS) (*Vocabulary, error) {
+	patterns := []struct {
+		Pattern string
+		Func    func(fs.FS) (*Vocabulary, error)
+	}{
+		{"tokenizer.model", parseSentencePiece},
+		{"tokenizer.json", parseVocabularyFromTokenizer},
+	}
+
+	for _, pattern := range patterns {
+		if _, err := fs.Stat(fsys, pattern.Pattern); errors.Is(err, os.ErrNotExist) {
+			continue
+		} else if err != nil {
+			return nil, err
+		}
+
+		return pattern.Func(fsys)
+	}
+
+	return nil, errors.New("unknown tensor format")
+}
+
+type SpecialVocabulary struct {
+	Type     string
+	ID       int
+	Content  string
+	AddToken bool
+}
+
+func (sv SpecialVocabulary) Key() string {
+	switch t := sv.Type; t {
+	case "bos", "eos", "cls", "mask":
+		return t
+	case "unk":
+		return "unknown"
+	case "sep":
+		//nolint:misspell // this is an upstream typo
+		return "seperator"
+	case "pad":
+		return "padding"
+	}
+
+	panic("unknown special vocabulary type")
+}

convert/tokenizer_spm.go

@@ -0,0 +1,83 @@
+package convert
+
+import (
+	"cmp"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"io/fs"
+	"os"
+	"slices"
+
+	"google.golang.org/protobuf/proto"
+
+	"github.com/ollama/ollama/convert/sentencepiece"
+)
+
+func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
+	bts, err := fs.ReadFile(fsys, "tokenizer.model")
+	if err != nil {
+		return nil, err
+	}
+
+	var spm sentencepiece.ModelProto
+	if err := proto.Unmarshal(bts, &spm); err != nil {
+		return nil, err
+	}
+
+	v := Vocabulary{Model: "llama"}
+	for _, piece := range spm.GetPieces() {
+		v.Tokens = append(v.Tokens, piece.GetPiece())
+		v.Scores = append(v.Scores, piece.GetScore())
+
+		switch t := piece.GetType(); t {
+		case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
+			sentencepiece.ModelProto_SentencePiece_CONTROL,
+			sentencepiece.ModelProto_SentencePiece_UNUSED,
+			sentencepiece.ModelProto_SentencePiece_BYTE:
+			v.Types = append(v.Types, int32(t))
+		default:
+			v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
+		}
+	}
+
+	f, err := fsys.Open("added_tokens.json")
+	if errors.Is(err, os.ErrNotExist) {
+		return &v, nil
+	} else if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var atm map[string]int
+	if err := json.NewDecoder(f).Decode(&atm); err != nil {
+		return nil, err
+	}
+
+	type t struct {
+		id      int
+		content string
+	}
+
+	var ts []t
+	for content, id := range atm {
+		ts = append(ts, t{id, content})
+	}
+
+	slices.SortFunc(ts, func(i, j t) int {
+		return cmp.Compare(i.id, j.id)
+	})
+
+	n := len(v.Tokens)
+	for i, t := range ts {
+		if t.id != i+n {
+			return nil, fmt.Errorf("invalid token id: %d", t.id)
+		}
+
+		v.Tokens = append(v.Tokens, t.content)
+		v.Scores = append(v.Scores, -1000.0)
+		v.Types = append(v.Types, tokenTypeUserDefined)
+	}
+
+	return &v, nil
+}

convert/torch.go

@@ -1,286 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"log/slog"
-	"os"
-	"path/filepath"
-	"regexp"
-	"strings"
-
-	"github.com/nlpodyssey/gopickle/pytorch"
-	"github.com/nlpodyssey/gopickle/types"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type torchWriterTo struct {
-	t *llm.Tensor
-
-	params *Params
-	bo     ByteOrder
-
-	storage pytorch.StorageInterface
-	handler func(w io.Writer, r torchWriterTo) error
-}
-
-type TorchFormat struct{}
-
-func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
-	slog.Debug("getting torch tensors")
-
-	files, err := filepath.Glob(filepath.Join(dirpath, "pytorch_model-*.bin"))
-	if err != nil {
-		slog.Error("didn't find any torch files")
-		return nil, err
-	}
-
-	var offset uint64
-
-	var tensors []llm.Tensor
-	for _, fn := range files {
-		m, err := pytorch.Load(fn)
-		if err != nil {
-			slog.Error(fmt.Sprintf("error unpickling: %q", err))
-			return []llm.Tensor{}, err
-		}
-
-		for _, k := range m.(*types.Dict).Keys() {
-			if strings.HasSuffix(k.(string), "self_attn.rotary_emb.inv_freq") {
-				continue
-			}
-
-			t, _ := m.(*types.Dict).Get(k)
-			tshape := t.(*pytorch.Tensor).Size
-
-			var size uint64
-			var kind uint32
-			switch len(tshape) {
-			case 0:
-				continue
-			case 1:
-				// convert to float32
-				kind = 0
-				size = uint64(tshape[0] * 4)
-			case 2:
-				// convert to float16
-				kind = 1
-				size = uint64(tshape[0] * tshape[1] * 2)
-			}
-
-			ggufName, err := tf.GetLayerName(k.(string))
-			if err != nil {
-				slog.Error(err.Error())
-				return nil, err
-			}
-			slog.Debug(fmt.Sprintf("finding name for '%s' -> '%s'", k.(string), ggufName))
-
-			shape := []uint64{0, 0, 0, 0}
-			for i := range tshape {
-				shape[i] = uint64(tshape[i])
-			}
-
-			tensor := llm.Tensor{
-				Name:   ggufName,
-				Kind:   kind,
-				Offset: offset, // calculate the offset
-				Shape:  shape[:],
-			}
-
-			tensor.WriterTo = torchWriterTo{
-				t:       &tensor,
-				params:  params,
-				bo:      params.ByteOrder,
-				storage: t.(*pytorch.Tensor).Source,
-			}
-
-			tensors = append(tensors, tensor)
-			offset += size
-		}
-	}
-
-	return tensors, nil
-
-}
-
-func getAltParams(dirpath string) (*Params, error) {
-	f, err := os.Open(filepath.Join(dirpath, "params.json"))
-	if err != nil {
-		slog.Error("no params.json")
-		return nil, err
-	}
-	defer f.Close()
-
-	type TorchParams struct {
-		HiddenSize     int     `json:"dim"`
-		AttentionHeads int     `json:"n_heads"`
-		KeyValHeads    int     `json:"n_kv_heads"`
-		HiddenLayers   int     `json:"n_layers"`
-		RopeTheta      int     `json:"rope_theta"`
-		NormEPS        float64 `json:"norm_eps"`
-	}
-
-	var tparams TorchParams
-
-	d := json.NewDecoder(f)
-	err = d.Decode(&tparams)
-	if err != nil {
-		return nil, err
-	}
-
-	params := &Params{
-		HiddenSize:     tparams.HiddenSize,
-		AttentionHeads: tparams.AttentionHeads,
-		KeyValHeads:    tparams.KeyValHeads,
-		HiddenLayers:   tparams.HiddenLayers,
-		NormEPS:        tparams.NormEPS,
-	}
-
-	switch {
-	case tparams.RopeTheta == 1000000:
-		// Codellama
-		params.ContextSize = 16384
-	case tparams.NormEPS == 1e-06:
-		// llama2
-		slog.Debug("Found llama2 - setting context size to 4096")
-		params.ContextSize = 4096
-	default:
-		params.ContextSize = 2048
-	}
-
-	params.ByteOrder = binary.LittleEndian
-	return params, nil
-}
-
-func (m *TorchFormat) GetParams(dirpath string) (*Params, error) {
-	f, err := os.Open(filepath.Join(dirpath, "config.json"))
-	if err != nil {
-		if os.IsNotExist(err) {
-			// try params.json instead
-			return getAltParams(dirpath)
-		} else {
-			return nil, err
-		}
-	}
-
-	var params Params
-	d := json.NewDecoder(f)
-	err = d.Decode(&params)
-	if err != nil {
-		return nil, err
-	}
-
-	params.ByteOrder = binary.LittleEndian
-	return &params, nil
-}
-
-func (m *TorchFormat) GetLayerName(n string) (string, error) {
-	directMap := map[string]string{
-		"tok_embeddings.weight":     "token_embd.weight",
-		"output.weight":             "output.weight",
-		"norm.weight":               "output_norm.weight",
-		"rope.freqs":                "rope_freqs.weight",
-		"model.embed_tokens.weight": "token_embd.weight",
-		"lm_head.weight":            "output.weight",
-		"model.norm.weight":         "output_norm.weight",
-	}
-
-	lMap := map[string]string{
-		"layers.(\\d+).attention_norm.weight":                 "blk.$1.attn_norm.weight",
-		"layers.(\\d+).attention_output_norm.weight":          "blk.$1.attn_norm.weight",
-		"layers.(\\d+).feed_forward.w2.weight":                "blk.$1.ffn_down.weight",
-		"layers.(\\d+).feed_forward.w1.weight":                "blk.$1.ffn_gate.weight",
-		"layers.(\\d+).feed_forward.w3.weight":                "blk.$1.ffn_up.weight",
-		"layers.(\\d+).ffn_norm.weight":                       "blk.$1.ffn_norm.weight",
-		"layers.(\\d+).attention.wk.weight":                   "blk.$1.attn_k.weight",
-		"layers.(\\d+).attention.wo.weight":                   "blk.$1.attn_output.weight",
-		"layers.(\\d+).attention.wq.weight":                   "blk.$1.attn_q.weight",
-		"layers.(\\d+).attention.wv.weight":                   "blk.$1.attn_v.weight",
-		"model.layers.(\\d+).input_layernorm.weight":          "blk.$1.attn_norm.weight",
-		"model.layers.(\\d+).mlp.down_proj.weight":            "blk.$1.ffn_down.weight",
-		"model.layers.(\\d+).mlp.gate_proj.weight":            "blk.$1.ffn_gate.weight",
-		"model.layers.(\\d+).mlp.up_proj.weight":              "blk.$1.ffn_up.weight",
-		"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
-		"model.layers.(\\d+).self_attn.k_proj.weight":         "blk.$1.attn_k.weight",
-		"model.layers.(\\d+).self_attn.o_proj.weight":         "blk.$1.attn_output.weight",
-		"model.layers.(\\d+).self_attn.q_proj.weight":         "blk.$1.attn_q.weight",
-		"model.layers.(\\d+).self_attn.v_proj.weight":         "blk.$1.attn_v.weight",
-	}
-
-	v, ok := directMap[n]
-	if ok {
-		return v, nil
-	}
-
-	// quick hack to rename the layers to gguf format
-	for k, v := range lMap {
-		re := regexp.MustCompile(k)
-		newName := re.ReplaceAllString(n, v)
-		if newName != n {
-			return newName, nil
-		}
-	}
-
-	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
-}
-
-func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
-	// use the handler if one is present
-	if r.handler != nil {
-		return 0, r.handler(w, r)
-	}
-
-	switch r.storage.(type) {
-	case *pytorch.FloatStorage:
-		slog.Warn(fmt.Sprintf("unexpected storage found for layer '%s'; skipping", r.t.Name))
-		return 0, nil
-	case *pytorch.HalfStorage:
-		switch r.t.Kind {
-		case 0:
-			data := r.storage.(*pytorch.HalfStorage).Data
-			slog.Debug(fmt.Sprintf("%35s F32 (%d)", r.t.Name, len(data)))
-			if err := binary.Write(w, r.bo, data); err != nil {
-				return 0, err
-			}
-		case 1:
-			data := r.storage.(*pytorch.HalfStorage).Data
-			tData := make([]uint16, len(data))
-			for cnt, v := range data {
-				tData[cnt] = uint16(float16.Fromfloat32(v))
-			}
-			slog.Debug(fmt.Sprintf("%35s F16 (%d)", r.t.Name, len(tData)))
-			if err := binary.Write(w, r.bo, tData); err != nil {
-				return 0, err
-			}
-		}
-	}
-
-	return 0, nil
-}
-
-func (m *TorchFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
-	switch len(params.Architectures) {
-	case 0:
-		return nil, fmt.Errorf("No architecture specified to convert")
-	case 1:
-		switch params.Architectures[0] {
-		case "LlamaForCausalLM":
-			return &LlamaModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		default:
-			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
-		}
-	}
-
-	return nil, fmt.Errorf("Unknown error")
-}

docs/api.md

@@ -12,6 +12,7 @@
 - [Pull a Model](#pull-a-model)
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
+- [List Running Models](#list-running-models)
 
 ## Conventions
 
@@ -25,7 +26,7 @@ All durations are returned in nanoseconds.
 
 ### Streaming responses
 
-Certain endpoints stream responses as JSON objects and can optional return non-streamed responses.
+Certain endpoints stream responses as JSON objects. Streaming can be disabled by providing `{"stream": false}` for these endpoints.
 
 ## Generate a completion
 
@@ -39,6 +40,7 @@ Generate a response for a given prompt with a provided model. This is a streamin
 
 - `model`: (required) the [model name](#model-names)
 - `prompt`: the prompt to generate a response for
+- `suffix`: the text after the model response
 - `images`: (optional) a list of base64-encoded images (for multimodal models such as `llava`)
 
 Advanced parameters (optional):
@@ -56,7 +58,8 @@ Advanced parameters (optional):
 
 Enable JSON mode by setting the `format` parameter to `json`. This will structure the response as a valid JSON object. See the JSON mode [example](#request-json-mode) below.
 
-> Note: it's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
+> [!IMPORTANT]
+> It's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
 
 ### Examples
 
@@ -147,8 +150,44 @@ If `stream` is set to `false`, the response will be a single JSON object:
 }
 ```
 
+#### Request (with suffix)
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/generate -d '{
+  "model": "codellama:code",
+  "prompt": "def compute_gcd(a, b):",
+  "suffix": "    return result",
+  "options": {
+    "temperature": 0
+  },
+  "stream": false
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "codellama:code",
+  "created_at": "2024-07-22T20:47:51.147561Z",
+  "response": "\n  if a == 0:\n    return b\n  else:\n    return compute_gcd(b % a, a)\n\ndef compute_lcm(a, b):\n  result = (a * b) / compute_gcd(a, b)\n",
+  "done": true,
+  "done_reason": "stop",
+  "context": [...],
+  "total_duration": 1162761250,
+  "load_duration": 6683708,
+  "prompt_eval_count": 17,
+  "prompt_eval_duration": 201222000,
+  "eval_count": 63,
+  "eval_duration": 953997000
+}
+```
+
 #### Request (JSON mode)
 
+> [!IMPORTANT]
 > When `format` is set to `json`, the output will always be a well-formed JSON object. It's important to also instruct the model to respond in JSON.
 
 ##### Request
@@ -249,7 +288,7 @@ curl http://localhost:11434/api/generate -d '{
 
 #### Request (Reproducible outputs)
 
-For reproducible outputs, set `temperature` to 0 and `seed` to a number:
+For reproducible outputs, set `seed` to a number:
 
 ##### Request
 
@@ -258,8 +297,7 @@ curl http://localhost:11434/api/generate -d '{
   "model": "mistral",
   "prompt": "Why is the sky blue?",
   "options": {
-    "seed": 123,
-    "temperature": 0
+    "seed": 123
   }
 }'
 ```
@@ -298,6 +336,7 @@ curl http://localhost:11434/api/generate -d '{
     "num_predict": 100,
     "top_k": 20,
     "top_p": 0.9,
+    "min_p": 0.0,
     "tfs_z": 0.5,
     "typical_p": 0.7,
     "repeat_last_n": 33,
@@ -380,12 +419,14 @@ Generate the next message in a chat with a provided model. This is a streaming e
 
 - `model`: (required) the [model name](#model-names)
 - `messages`: the messages of the chat, this can be used to keep a chat memory
+- `tools`: tools for the model to use if supported. Requires `stream` to be set to `false`
 
 The `message` object has the following fields:
 
-- `role`: the role of the message, either `system`, `user` or `assistant`
+- `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
 - `content`: the content of the message
 - `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
+- `tool_calls` (optional): a list of tools the model wants to use
 
 Advanced parameters (optional):
 
@@ -546,7 +587,7 @@ Final response:
 
 ##### Request
 
-Send a chat message with a conversation history.
+Send a chat message with images. The images should be provided as an array, with the individual images encoded in Base64.
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
@@ -622,6 +663,79 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
+#### Chat request (with tools)
+
+##### Request
+
+```
+curl http://localhost:11434/api/chat -d '{
+  "model": "mistral",
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the weather today in Paris?"
+    }
+  ],
+  "stream": false,
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "location": {
+              "type": "string",
+              "description": "The location to get the weather for, e.g. San Francisco, CA"
+            },
+            "format": {
+              "type": "string",
+              "description": "The format to return the weather in, e.g. 'celsius' or 'fahrenheit'",
+              "enum": ["celsius", "fahrenheit"]
+            }
+          },
+          "required": ["location", "format"]
+        }
+      }
+    }
+  ]
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "mistral:7b-instruct-v0.3-q4_K_M",
+  "created_at": "2024-07-22T20:33:28.123648Z",
+  "message": {
+    "role": "assistant",
+    "content": "",
+    "tool_calls": [
+      {
+        "function": {
+          "name": "get_current_weather",
+          "arguments": {
+            "format": "celsius",
+            "location": "Paris, FR"
+          }
+        }
+      }
+    ]
+  },
+  "done_reason": "stop",
+  "done": true,
+  "total_duration": 885095291,
+  "load_duration": 3753500,
+  "prompt_eval_count": 122,
+  "prompt_eval_duration": 328493000,
+  "eval_count": 33,
+  "eval_duration": 552222000
+}
+```
+
 ## Create a Model
 
 ```shell
@@ -777,11 +891,12 @@ A single JSON object will be returned.
 POST /api/show
 ```
 
-Show information about a model including details, modelfile, template, parameters, license, and system prompt.
+Show information about a model including details, modelfile, template, parameters, license, system prompt.
 
 ### Parameters
 
 - `name`: name of the model to show
+- `verbose`: (optional) if set to `true`, returns full data for verbose response fields
 
 ### Examples
 
@@ -797,15 +912,41 @@ curl http://localhost:11434/api/show -d '{
 
 ```json
 {
-  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSSISTANT:\"",
-  "parameters": "num_ctx                        4096\nstop                           \u003c/s\u003e\nstop                           USER:\nstop                           ASSSISTANT:",
-  "template": "{{ .System }}\nUSER: {{ .Prompt }}\nASSSISTANT: ",
+  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
+  "parameters": "num_keep                       24\nstop                           \"<|start_header_id|>\"\nstop                           \"<|end_header_id|>\"\nstop                           \"<|eot_id|>\"",
+  "template": "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>",
   "details": {
+    "parent_model": "",
     "format": "gguf",
     "family": "llama",
-    "families": ["llama", "clip"],
-    "parameter_size": "7B",
+    "families": [
+      "llama"
+    ],
+    "parameter_size": "8.0B",
     "quantization_level": "Q4_0"
+  },
+  "model_info": {
+    "general.architecture": "llama",
+    "general.file_type": 2,
+    "general.parameter_count": 8030261248,
+    "general.quantization_version": 2,
+    "llama.attention.head_count": 32,
+    "llama.attention.head_count_kv": 8,
+    "llama.attention.layer_norm_rms_epsilon": 0.00001,
+    "llama.block_count": 32,
+    "llama.context_length": 8192,
+    "llama.embedding_length": 4096,
+    "llama.feed_forward_length": 14336,
+    "llama.rope.dimension_count": 128,
+    "llama.rope.freq_base": 500000,
+    "llama.vocab_size": 128256,
+    "tokenizer.ggml.bos_token_id": 128000,
+    "tokenizer.ggml.eos_token_id": 128009,
+    "tokenizer.ggml.merges": [],            // populates if `verbose=true`
+    "tokenizer.ggml.model": "gpt2",
+    "tokenizer.ggml.pre": "llama-bpe",
+    "tokenizer.ggml.token_type": [],        // populates if `verbose=true`
+    "tokenizer.ggml.tokens": []             // populates if `verbose=true`
   }
 }
 ```
@@ -998,6 +1139,118 @@ If `stream` is set to `false`, then the response is a single JSON object:
 
 ## Generate Embeddings
 
+```shell
+POST /api/embed
+```
+
+Generate embeddings from a model
+
+### Parameters
+
+- `model`: name of model to generate embeddings from
+- `input`: text or list of text to generate embeddings for
+
+Advanced parameters:
+
+- `truncate`: truncates the end of each input to fit within context length. Returns error if `false` and context length is exceeded. Defaults to `true`
+- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
+- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
+
+### Examples
+
+#### Request
+
+```shell
+curl http://localhost:11434/api/embed -d '{
+  "model": "all-minilm",
+  "input": "Why is the sky blue?"
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "all-minilm",
+  "embeddings": [[
+    0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
+    0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
+  ]]
+}
+```
+
+#### Request (Multiple input)
+
+```shell
+curl http://localhost:11434/api/embed -d '{
+  "model": "all-minilm",
+  "input": ["Why is the sky blue?", "Why is the grass green?"]
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "all-minilm",
+  "embeddings": [[
+    0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
+    0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
+  ],[
+    -0.0098027075, 0.06042469, 0.025257962, -0.006364387, 0.07272725,
+    0.017194884, 0.09032035, -0.051705178, 0.09951512, 0.09072481
+  ]]
+}
+```
+
+## List Running Models
+```shell
+GET /api/ps
+```
+
+List models that are currently loaded into memory.
+
+#### Examples
+
+### Request
+
+```shell
+curl http://localhost:11434/api/ps
+```
+
+#### Response
+
+A single JSON object will be returned.
+
+```json
+{
+  "models": [
+    {
+      "name": "mistral:latest",
+      "model": "mistral:latest",
+      "size": 5137025024,
+      "digest": "2ae6f6dd7a3dd734790bbbf58b8909a606e0e7e97e94b7604e0aa7ae4490e6d8",
+      "details": {
+        "parent_model": "",
+        "format": "gguf",
+        "family": "llama",
+        "families": [
+          "llama"
+        ],
+        "parameter_size": "7.2B",
+        "quantization_level": "Q4_0"
+      },
+      "expires_at": "2024-06-04T14:38:31.83753-07:00",
+      "size_vram": 5137025024
+    }
+  ]
+}
+```
+
+## Generate Embedding
+
+> Note: this endpoint has been superseded by `/api/embed`
+
 ```shell
 POST /api/embeddings
 ```

docs/development.md

@@ -6,6 +6,8 @@ Install required tools:
 - go version 1.22 or higher
 - gcc version 11.4.0 or higher
 
+### MacOS
+
 ```bash
 brew install go cmake gcc
 ```
@@ -102,7 +104,7 @@ like to use. For example, to compile an optimized binary for an Intel i9-9880H,
 you might use:
 
 ```
-OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on" go generate ./...
+OLLAMA_CUSTOM_CPU_DEFS="-DGGML_AVX=on -DGGML_AVX2=on -DGGML_F16C=on -DGGML_FMA=on" go generate ./...
 go build .
 ```
 
@@ -112,15 +114,18 @@ If you have Docker available, you can build linux binaries with `./scripts/build
 
 ### Windows
 
-Note: The windows build for Ollama is still under development.
+Note: The Windows build for Ollama is still under development.
 
-Install required tools:
+First, install required tools:
 
 - MSVC toolchain - C/C++ and cmake as minimal requirements
 - Go version 1.22 or higher
 - MinGW (pick one variant) with GCC.
   - [MinGW-w64](https://www.mingw-w64.org/)
   - [MSYS2](https://www.msys2.org/)
+- The `ThreadJob` Powershell module: `Install-Module -Name ThreadJob -Scope CurrentUser`
+
+Then, build the `ollama` binary:
 
 ```powershell
 $env:CGO_ENABLED="1"

docs/docker.md

@@ -63,7 +63,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
 Now you can run a model:
 
 ```
-docker exec -it ollama ollama run llama3
+docker exec -it ollama ollama run llama3.1
 ```
 
 ### Try different models

docs/faq.md

@@ -6,7 +6,7 @@ Ollama on macOS and Windows will automatically download updates. Click on the ta
 
 On Linux, re-run the install script:
 
-```
+```shell
 curl -fsSL https://ollama.com/install.sh | sh
 ```
 
@@ -30,7 +30,7 @@ To change this when using `ollama run`, use `/set parameter`:
 
 When using the API, specify the `num_ctx` parameter:
 
-```
+```shell
 curl http://localhost:11434/api/generate -d '{
   "model": "llama3",
   "prompt": "Why is the sky blue?",
@@ -40,6 +40,21 @@ curl http://localhost:11434/api/generate -d '{
 }'
 ```
 
+## How can I tell if my model was loaded onto the GPU?
+
+Use the `ollama ps` command to see what models are currently loaded into memory.
+
+```shell
+ollama ps
+NAME      	ID          	SIZE 	PROCESSOR	UNTIL
+llama3:70b	bcfb190ca3a7	42 GB	100% GPU 	4 minutes from now
+```
+
+The `Processor` column will show which memory the model was loaded in to:
+* `100% GPU` means the model was loaded entirely into the GPU
+* `100% CPU` means the model was loaded entirely in system memory
+* `48%/52% CPU/GPU` means the model was loaded partially onto both the GPU and into system memory
+
 ## How do I configure Ollama server?
 
 Ollama server can be configured with environment variables.
@@ -80,81 +95,19 @@ If Ollama is run as a systemd service, environment variables should be set using
 
 ### Setting environment variables on Windows
 
-On windows, Ollama inherits your user and system environment variables.
+On Windows, Ollama inherits your user and system environment variables.
 
-1. First Quit Ollama by clicking on it in the task bar
+1. First Quit Ollama by clicking on it in the task bar.
 
-2. Edit system environment variables from the control panel
+2. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
 
-3. Edit or create New variable(s) for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
+3. Click on _Edit environment variables for your account_.
 
-4. Click OK/Apply to save
+4. Edit or create a new variable for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
 
-5. Run `ollama` from a new terminal window
+5. Click OK/Apply to save.
 
-
-## How can I expose Ollama on my network?
-
-Ollama binds 127.0.0.1 port 11434 by default. Change the bind address with the `OLLAMA_HOST` environment variable.
-
-Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
-
-## How can I use Ollama with a proxy server?
-
-Ollama runs an HTTP server and can be exposed using a proxy server such as Nginx. To do so, configure the proxy to forward requests and optionally set required headers (if not exposing Ollama on the network). For example, with Nginx:
-
-```
-server {
-    listen 80;
-    server_name example.com;  # Replace with your domain or IP
-    location / {
-        proxy_pass http://localhost:11434;
-        proxy_set_header Host localhost:11434;
-    }
-}
-```
-
-## How can I use Ollama with ngrok?
-
-Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
-
-```
-ngrok http 11434 --host-header="localhost:11434"
-```
-
-## How can I use Ollama with Cloudflare Tunnel?
-
-To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
-
-```
-cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434"
-```
-
-## How can I allow additional web origins to access Ollama?
-
-Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
-
-Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
-
-## Where are models stored?
-
-- macOS: `~/.ollama/models`
-- Linux: `/usr/share/ollama/.ollama/models`
-- Windows: `C:\Users\%username%\.ollama\models`
-
-### How do I set them to a different location?
-
-If a different directory needs to be used, set the environment variable `OLLAMA_MODELS` to the chosen directory.
-
-Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
-
-## Does Ollama send my prompts and answers back to ollama.com?
-
-No. Ollama runs locally, and conversation data does not leave your machine.
-
-## How can I use Ollama in Visual Studio Code?
-
-There is already a large collection of plugins available for VSCode as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
+6. Start the Ollama application from the Windows Start menu.
 
 ## How do I use Ollama behind a proxy?
 
@@ -181,6 +134,69 @@ docker build -t ollama-with-ca .
 docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
 ```
 
+## Does Ollama send my prompts and answers back to ollama.com?
+
+No. Ollama runs locally, and conversation data does not leave your machine.
+
+## How can I expose Ollama on my network?
+
+Ollama binds 127.0.0.1 port 11434 by default. Change the bind address with the `OLLAMA_HOST` environment variable.
+
+Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
+
+## How can I use Ollama with a proxy server?
+
+Ollama runs an HTTP server and can be exposed using a proxy server such as Nginx. To do so, configure the proxy to forward requests and optionally set required headers (if not exposing Ollama on the network). For example, with Nginx:
+
+```
+server {
+    listen 80;
+    server_name example.com;  # Replace with your domain or IP
+    location / {
+        proxy_pass http://localhost:11434;
+        proxy_set_header Host localhost:11434;
+    }
+}
+```
+
+## How can I use Ollama with ngrok?
+
+Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
+
+```shell
+ngrok http 11434 --host-header="localhost:11434"
+```
+
+## How can I use Ollama with Cloudflare Tunnel?
+
+To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
+
+```shell
+cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434"
+```
+
+## How can I allow additional web origins to access Ollama?
+
+Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
+
+Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
+
+## Where are models stored?
+
+- macOS: `~/.ollama/models`
+- Linux: `/usr/share/ollama/.ollama/models`
+- Windows: `C:\Users\%username%\.ollama\models`
+
+### How do I set them to a different location?
+
+If a different directory needs to be used, set the environment variable `OLLAMA_MODELS` to the chosen directory.
+
+Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
+
+## How can I use Ollama in Visual Studio Code?
+
+There is already a large collection of plugins available for VSCode as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
+
 ## How do I use Ollama with GPU acceleration in Docker?
 
 The Ollama Docker container can be configured with GPU acceleration in Linux or Windows (with WSL2). This requires the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit). See [ollama/ollama](https://hub.docker.com/r/ollama/ollama) for more details.
@@ -195,7 +211,7 @@ Open `Control Panel > Networking and Internet > View network status and tasks` a
 Click on `Configure` and open the `Advanced` tab. Search through each of the properties until you find `Large Send Offload Version 2 (IPv4)` and `Large Send Offload Version 2 (IPv6)`. *Disable* both of these
 properties.
 
-## How can I pre-load a model to get faster response times?
+## How can I preload a model into Ollama to get faster response times?
 
 If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
 
@@ -209,6 +225,11 @@ To use the chat completions endpoint, use:
 curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
 ```
 
+To preload a model using the CLI, use the command:
+```shell
+ollama run llama3.1 ""
+```
+
 ## How do I keep a model loaded in memory or make it unload immediately?
 
 By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you are making numerous requests to the LLM. You may, however, want to free up the memory before the 5 minutes have elapsed or keep the model loaded indefinitely. Use the `keep_alive` parameter with either the `/api/generate` and `/api/chat` API endpoints to control how long the model is left in memory.
@@ -233,8 +254,26 @@ Alternatively, you can change the amount of time all models are loaded into memo
 
 If you wish to override the `OLLAMA_KEEP_ALIVE` setting, use the `keep_alive` API parameter with the `/api/generate` or `/api/chat` API endpoints.
 
-## How do I manage the maximum number of requests the server can queue
+## How do I manage the maximum number of requests the Ollama server can queue?
 
-If too many requests are sent to the server, it will respond with a 503 error
-indicating the server is overloaded.  You can adjust how many requests may be
-queue by setting `OLLAMA_MAX_QUEUE`
+If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded.  You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.
+
+## How does Ollama handle concurrent requests?
+
+Ollama supports two levels of concurrent processing.  If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time.  For a given model, if there is sufficient available memory when the model is loaded, it is configured to allow parallel request processing.
+
+If there is insufficient available memory to load a new model request while one or more models are already loaded, all new requests will be queued until the new model can be loaded.  As prior models become idle, one or more will be unloaded to make room for the new model.  Queued requests will be processed in order.  When using GPU inference new models must be able to completely fit in VRAM to allow concurrent model loads.
+
+Parallel request processing for a given model results in increasing the context size by the number of parallel requests.  For example, a 2K context with 4 parallel requests will result in an 8K context and additional memory allocation.
+
+The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
+
+- `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory.  The default is 3 * the number of GPUs or 3 for CPU inference.
+- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default will auto-select either 4 or 1 based on available memory.
+- `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
+
+Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
+
+## How does Ollama load models on multiple GPUs?
+
+Installing multiple GPUs of the same brand can be a great way to increase your available VRAM to load larger models.  When you load a new model, Ollama evaluates the required VRAM for the model against what is currently available.  If the model will entirely fit on any single GPU, Ollama will load the model on that GPU.  This typically provides the best performance as it reduces the amount of data transfering across the PCI bus during inference.  If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.

docs/gpu.md

@@ -8,7 +8,7 @@ Check your compute compatibility to see if your card is supported:
 | Compute Capability | Family              | Cards                                                                                                       |
 | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
 | 9.0                | NVIDIA              | `H100`                                                                                                      |
-| 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080` `RTX 4070 Ti` `RTX 4060 Ti`                                                           |
+| 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |
 | 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060`         |
 |                    | NVIDIA Professional | `A40` `RTX A6000` `RTX A5000` `RTX A4000` `RTX A3000` `RTX A2000` `A10` `A16` `A2`                          |
@@ -18,7 +18,7 @@ Check your compute compatibility to see if your card is supported:
 |                    | Quadro              | `RTX 8000` `RTX 6000` `RTX 5000` `RTX 4000`                                                                 |
 | 7.0                | NVIDIA              | `TITAN V` `V100` `Quadro GV100`                                                                             |
 | 6.1                | NVIDIA TITAN        | `TITAN Xp` `TITAN X`                                                                                        |
-|                    | GeForce GTX         | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050`                                     |
+|                    | GeForce GTX         | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050 Ti` `GTX 1050`                       |
 |                    | Quadro              | `P6000` `P5200` `P4200` `P3200` `P5000` `P4000` `P3000` `P2200` `P2000` `P1000` `P620` `P600` `P500` `P520` |
 |                    | Tesla               | `P40` `P4`                                                                                                  |
 | 6.0                | NVIDIA              | `Tesla P100` `Quadro GP100`                                                                                 |
@@ -46,13 +46,24 @@ sudo modprobe nvidia_uvm`
 
 ## AMD Radeon
 Ollama supports the following AMD GPUs:
+
+### Linux Support
 | Family         | Cards and accelerators                                                                                                               |
 | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
 | AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64` `Vega 56`    |
 | AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `VII` `SSG` |
 | AMD Instinct   | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60` `MI50`                                                               |
 
-### Overrides
+### Windows Support
+With ROCm v6.1, the following GPUs are supported on Windows.
+
+| Family         | Cards and accelerators                                                                                                               |
+| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
+| AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800`    |
+| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` |
+
+
+### Overrides on Linux
 Ollama leverages the AMD ROCm library, which does not support all AMD GPUs. In
 some cases you can force the system to try to use a similar LLVM target that is
 close.  For example The Radeon RX 5400 is `gfx1034` (also known as 10.3.4)
@@ -63,7 +74,7 @@ would set `HSA_OVERRIDE_GFX_VERSION="10.3.0"` as an environment variable for the
 server.  If you have an unsupported AMD GPU you can experiment using the list of
 supported types below.
 
-At this time, the known supported GPU types are the following LLVM Targets.
+At this time, the known supported GPU types on linux are the following LLVM Targets.
 This table shows some example GPUs that map to these LLVM targets:
 | **LLVM Target** | **An Example GPU** |
 |-----------------|---------------------|

docs/import.md

@@ -1,170 +1,88 @@
-# Import a model
+# Import
 
-This guide walks through importing a GGUF, PyTorch or Safetensors model.
+GGUF models and select Safetensors models can be imported directly into Ollama.
 
-## Importing (GGUF)
+## Import GGUF
 
-### Step 1: Write a `Modelfile`
+A binary GGUF file can be imported directly into Ollama through a Modelfile.
 
-Start by creating a `Modelfile`. This file is the blueprint for your model, specifying weights, parameters, prompt templates and more.
-
-```
-FROM ./mistral-7b-v0.1.Q4_0.gguf
+```dockerfile
+FROM /path/to/file.gguf
 ```
 
-(Optional) many chat models require a prompt template in order to answer correctly. A default prompt template can be specified with the `TEMPLATE` instruction in the `Modelfile`:
+## Import Safetensors
 
-```
-FROM ./mistral-7b-v0.1.Q4_0.gguf
-TEMPLATE "[INST] {{ .Prompt }} [/INST]"
+If the model being imported is one of these architectures, it can be imported directly into Ollama through a Modelfile:
+
+ - LlamaForCausalLM
+ - MistralForCausalLM
+ - GemmaForCausalLM
+
+```dockerfile
+FROM /path/to/safetensors/directory
 ```
 
-### Step 2: Create the Ollama model
+For architectures not directly convertable by Ollama, see llama.cpp's [guide](https://github.com/ggerganov/llama.cpp/blob/master/README.md#prepare-and-quantize) on conversion. After conversion, see [Import GGUF](#import-gguf).
 
-Finally, create a model from your `Modelfile`:
+## Automatic Quantization
 
+> [!NOTE]
+> Automatic quantization requires v0.1.35 or higher.
+
+Ollama is capable of quantizing FP16 or FP32 models to any of the supported quantizations with the `-q/--quantize` flag in `ollama create`.
+
+```dockerfile
+FROM /path/to/my/gemma/f16/model
 ```
-ollama create example -f Modelfile
-```
-
-### Step 3: Run your model
-
-Next, test the model with `ollama run`:
-
-```
-ollama run example "What is your favourite condiment?"
-```
-
-## Importing (PyTorch & Safetensors)
-
-> Importing from PyTorch and Safetensors is a longer process than importing from GGUF. Improvements that make it easier are a work in progress.
-
-### Setup
-
-First, clone the `ollama/ollama` repo:
-
-```
-git clone git@github.com:ollama/ollama.git ollama
-cd ollama
-```
-
-and then fetch its `llama.cpp` submodule:
 
 ```shell
-git submodule init
-git submodule update llm/llama.cpp
+$ ollama create -q Q4_K_M mymodel
+transferring model data
+quantizing F16 model to Q4_K_M
+creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
+creating new layer sha256:0853f0ad24e5865173bbf9ffcc7b0f5d56b66fd690ab1009867e45e7d2c4db0f
+writing manifest
+success
 ```
 
-Next, install the Python dependencies:
+### Supported Quantizations
 
-```
-python3 -m venv llm/llama.cpp/.venv
-source llm/llama.cpp/.venv/bin/activate
-pip install -r llm/llama.cpp/requirements.txt
+- `Q4_0`
+- `Q4_1`
+- `Q5_0`
+- `Q5_1`
+- `Q8_0`
+
+#### K-means Quantizations
+
+- `Q3_K_S`
+- `Q3_K_M`
+- `Q3_K_L`
+- `Q4_K_S`
+- `Q4_K_M`
+- `Q5_K_S`
+- `Q5_K_M`
+- `Q6_K`
+
+## Template Detection
+
+> [!NOTE]
+> Template detection requires v0.1.42 or higher.
+
+Ollama uses model metadata, specifically `tokenizer.chat_template`, to automatically create a template appropriate for the model you're importing.
+
+```dockerfile
+FROM /path/to/my/gemma/model
 ```
 
-Then build the `quantize` tool:
-
-```
-make -C llm/llama.cpp quantize
+```shell
+$ ollama create mymodel
+transferring model data
+using autodetected template gemma-instruct
+creating new layer sha256:baa2a0edc27d19cc6b7537578a9a7ba1a4e3214dc185ed5ae43692b319af7b84
+creating new layer sha256:ba66c3309914dbef07e5149a648fd1877f030d337a4f240d444ea335008943cb
+writing manifest
+success
 ```
 
-### Clone the HuggingFace repository (optional)
-
-If the model is currently hosted in a HuggingFace repository, first clone that repository to download the raw model.
-
-Install [Git LFS](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage), verify it's installed, and then clone the model's repository:
-
-```
-git lfs install
-git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 model
-```
-
-### Convert the model
-
-> Note: some model architectures require using specific convert scripts. For example, Qwen models require running `convert-hf-to-gguf.py` instead of `convert.py`
-
-```
-python llm/llama.cpp/convert.py ./model --outtype f16 --outfile converted.bin
-```
-
-### Quantize the model
-
-```
-llm/llama.cpp/quantize converted.bin quantized.bin q4_0
-```
-
-### Step 3: Write a `Modelfile`
-
-Next, create a `Modelfile` for your model:
-
-```
-FROM quantized.bin
-TEMPLATE "[INST] {{ .Prompt }} [/INST]"
-```
-
-### Step 4: Create the Ollama model
-
-Finally, create a model from your `Modelfile`:
-
-```
-ollama create example -f Modelfile
-```
-
-### Step 5: Run your model
-
-Next, test the model with `ollama run`:
-
-```
-ollama run example "What is your favourite condiment?"
-```
-
-## Publishing your model (optional – early alpha)
-
-Publishing models is in early alpha. If you'd like to publish your model to share with others, follow these steps:
-
-1. Create [an account](https://ollama.com/signup)
-2. Copy your Ollama public key:
-  - macOS: `cat ~/.ollama/id_ed25519.pub | pbcopy`
-  - Windows: `type %USERPROFILE%\.ollama\id_ed25519.pub`
-  - Linux: `cat /usr/share/ollama/.ollama/id_ed25519.pub`
-3. Add your public key to your [Ollama account](https://ollama.com/settings/keys)
-
-Next, copy your model to your username's namespace:
-
-```
-ollama cp example <your username>/example
-```
-
-> Note: model names may only contain lowercase letters, digits, and the characters `.`, `-`, and `_`.
-
-Then push the model:
-
-```
-ollama push <your username>/example
-```
-
-After publishing, your model will be available at `https://ollama.com/<your username>/example`.
-
-## Quantization reference
-
-The quantization options are as follow (from highest highest to lowest levels of quantization). Note: some architectures such as Falcon do not support K quants.
-
-- `q2_K`
-- `q3_K`
-- `q3_K_S`
-- `q3_K_M`
-- `q3_K_L`
-- `q4_0` (recommended)
-- `q4_1`
-- `q4_K`
-- `q4_K_S`
-- `q4_K_M`
-- `q5_0`
-- `q5_1`
-- `q5_K`
-- `q5_K_S`
-- `q5_K_M`
-- `q6_K`
-- `q8_0`
-- `f16`
+Defining a template in the Modelfile will disable this feature which may be useful if you want to use a different template than the autodetected one.

docs/linux.md

@@ -100,6 +100,16 @@ sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
 sudo chmod +x /usr/bin/ollama
 ```
 
+## Installing specific versions
+
+Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases). 
+
+For example:
+
+```
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.1.32 sh
+```
+
 ## Viewing logs
 
 To view logs of Ollama running as a startup service, run:

docs/modelfile.md

@@ -1,6 +1,7 @@
 # Ollama Model File
 
-> Note: `Modelfile` syntax is in development
+> [!NOTE]
+> `Modelfile` syntax is in development
 
 A model file is the blueprint to create and share models with Ollama.
 
@@ -140,6 +141,7 @@ PARAMETER <parameter> <parametervalue>
 | num_predict    | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)                                                                                                                                   | int        | num_predict 42       |
 | top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40             |
 | top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9            |
+| min_p          | Alternative to the top_p, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. (Default: 0.0) | float      | min_p 0.05            |
 
 ### TEMPLATE
 

docs/openai.md

@@ -27,6 +27,15 @@ chat_completion = client.chat.completions.create(
     ],
     model='llama3',
 )
+
+list_completion = client.models.list()
+
+model = client.models.retrieve("llama3")
+
+embeddings = client.embeddings.create(
+    model="all-minilm",
+    input=["why is the sky blue?", "why is the grass green?"]
+)
 ```
 
 ### OpenAI JavaScript library
@@ -45,6 +54,15 @@ const chatCompletion = await openai.chat.completions.create({
   messages: [{ role: 'user', content: 'Say this is a test' }],
   model: 'llama3',
 })
+
+const listCompletion = await openai.models.list()
+
+const model = await openai.models.retrieve("llama3");
+
+const embedding = await openai.embeddings.create({
+  model: "all-minilm",
+  input: ["why is the sky blue?", "why is the grass green?"],
+});
 ```
 
 ### `curl`
@@ -65,6 +83,17 @@ curl http://localhost:11434/v1/chat/completions \
             }
         ]
     }'
+
+curl http://localhost:11434/v1/models
+
+curl http://localhost:11434/v1/models/llama3
+
+curl http://localhost:11434/v1/embeddings \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "all-minilm",
+        "input": ["why is the sky blue?", "why is the grass green?"]
+    }'
 ```
 
 ## Endpoints
@@ -77,8 +106,8 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] Streaming
 - [x] JSON mode
 - [x] Reproducible outputs
+- [x] Tools (streaming support coming soon)
 - [ ] Vision
-- [ ] Function calling
 - [ ] Logprobs
 
 #### Supported request fields
@@ -96,17 +125,39 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
-- [ ] `logit_bias`
-- [ ] `tools`
+- [x] `tools`
 - [ ] `tool_choice`
+- [ ] `logit_bias`
 - [ ] `user`
 - [ ] `n`
 
+### `/v1/models`
+
 #### Notes
 
-- Setting `seed` will always set `temperature` to `0`
-- `finish_reason` will always be `stop`
-- `usage.prompt_tokens` will be 0 for completions where prompt evaluation is cached
+- `created` corresponds to when the model was last modified
+- `owned_by` corresponds to the ollama username, defaulting to `"library"`
+
+### `/v1/models/{model}`
+
+#### Notes
+
+- `created` corresponds to when the model was last modified
+- `owned_by` corresponds to the ollama username, defaulting to `"library"`
+
+### `/v1/embeddings`
+
+#### Supported request fields
+
+- [x] `model`
+- [x] `input`
+  - [x] string
+  - [x] array of strings
+  - [ ] array of tokens
+  - [ ] array of token arrays
+- [ ] `encoding format`
+- [ ] `dimensions`
+- [ ] `user`
 
 ## Models
 

docs/template.md

@@ -0,0 +1,173 @@
+# Template
+
+Ollama provides a powerful templating engine backed by Go's built-in templating engine to construct prompts for your large language model. This feature is a valuable tool to get the most out of your models.
+
+## Basic Template Structure
+
+A basic Go template consists of three main parts:
+
+* **Layout**: The overall structure of the template.
+* **Variables**: Placeholders for dynamic data that will be replaced with actual values when the template is rendered.
+* **Functions**: Custom functions or logic that can be used to manipulate the template's content.
+
+Here's an example of a simple chat template:
+
+```gotmpl
+{{- range .Messages }}
+{{ .Role }}: {{ .Content }}
+{{- end }}
+```
+
+In this example, we have:
+
+* A basic messages structure (layout)
+* Three variables: `Messages`, `Role`, and `Content` (variables)
+* A custom function (action) that iterates over an array of items (`range .Messages`) and displays each item
+
+## Adding templates to your model
+
+By default, models imported into Ollama have a default template of `{{ .Prompt }}`, i.e. user inputs are sent verbatim to the LLM. This is appropriate for text or code completion models but lacks essential markers for chat or instruction models.
+
+Omitting a template in these models puts the responsibility of correctly templating input onto the user. Adding a template allows users to easily get the best results from the model.
+
+To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
+
+```dockerfile
+FROM llama3
+
+TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>
+{{- end }}
+{{- range .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
+
+{{ .Content }}<|eot_id|>
+{{- end }}<|start_header_id|>assistant<|end_header_id|>
+
+"""
+```
+
+## Variables
+
+`System` (string): system prompt
+
+`Prompt` (string): user prompt
+
+`Response` (string): assistant response
+
+`Suffix` (string): text inserted after the assistant's response
+
+`Messages` (list): list of messages
+
+`Messages[].Role` (string): role which can be one of `system`, `user`, `assistant`, or `tool`
+
+`Messages[].Content` (string):  message content
+
+`Messages[].ToolCalls` (list): list of tools the model wants to call
+
+`Messages[].ToolCalls[].Function` (object): function to call
+
+`Messages[].ToolCalls[].Function.Name` (string): function name
+
+`Messages[].ToolCalls[].Function.Arguments` (map): mapping of argument name to argument value
+
+`Tools` (list): list of tools the model can access
+
+`Tools[].Type` (string): schema type. `type` is always `function`
+
+`Tools[].Function` (object): function definition
+
+`Tools[].Function.Name` (string): function name
+
+`Tools[].Function.Description` (string): function description
+
+`Tools[].Function.Parameters` (object): function parameters
+
+`Tools[].Function.Parameters.Type` (string): schema type. `type` is always `object`
+
+`Tools[].Function.Parameters.Required` (list): list of required properties
+
+`Tools[].Function.Parameters.Properties` (map): mapping of property name to property definition
+
+`Tools[].Function.Parameters.Properties[].Type` (string): property type
+
+`Tools[].Function.Parameters.Properties[].Description` (string): property description
+
+`Tools[].Function.Parameters.Properties[].Enum` (list): list of valid values
+
+## Tips and Best Practices
+
+Keep the following tips and best practices in mind when working with Go templates:
+
+* **Be mindful of dot**: Control flow structures like `range` and `with` changes the value `.`
+* **Out-of-scope variables**: Use `$.` to reference variables not currently in scope, starting from the root
+* **Whitespace control**: Use `-` to trim leading (`{{-`) and trailing (`-}}`) whitespace
+
+## Examples
+
+### Example Messages
+
+#### ChatML
+
+ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.
+
+```gotmpl
+{{- if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}
+{{- range .Messages }}<|im_start|>{{ .Role }}
+{{ .Content }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ else }}
+{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+```
+
+### Example Tools
+
+Tools support can be added to a model by adding a `{{ .Tools }}` node to the template. This feature is useful for models trained to call external tools and can a powerful tool for retrieving real-time data or performing complex tasks.
+
+#### Mistral
+
+Mistral v0.3 and Mixtral 8x22B supports tool calling.
+
+```gotmpl
+{{- range $index, $_ := .Messages }}
+{{- if eq .Role "user" }}
+{{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS] {{ json $.Tools }}[/AVAILABLE_TOOLS]
+{{- end }}[INST] {{ if and (eq (len (slice $.Messages $index)) 1) $.System }}{{ $.System }}
+
+{{ end }}{{ .Content }}[/INST]
+{{- else if eq .Role "assistant" }}
+{{- if .Content }} {{ .Content }}</s>
+{{- else if .ToolCalls }}[TOOL_CALLS] [
+{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ json .Function.Arguments }}}
+{{- end }}]</s>
+{{- end }}
+{{- else if eq .Role "tool" }}[TOOL_RESULTS] {"content": {{ .Content }}}[/TOOL_RESULTS]
+{{- end }}
+{{- end }}
+```
+
+### Example Fill-in-Middle
+
+Fill-in-middle support can be added to a model by adding a `{{ .Suffix }}` node to the template. This feature is useful for models that are trained to generate text in the middle of user input, such as code completion models.
+
+#### CodeLlama
+
+CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://ollama.com/library/codellama:13b-code) code completion models support fill-in-middle.
+
+```gotmpl
+<PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
+```
+
+> [!NOTE]
+> CodeLlama 34B and 70B code completion and all instruct and Python fine-tuned models do not support fill-in-middle.
+
+#### Codestral
+
+Codestral [22B](https://ollama.com/library/codestral:22b) supports fill-in-middle.
+
+```gotmpl
+[SUFFIX]{{ .Suffix }}[PREFIX] {{ .Prompt }}
+```

docs/troubleshooting.md

@@ -22,7 +22,7 @@ docker logs <container-name>
 If manually running `ollama serve` in a terminal, the logs will be on that terminal.
 
 When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
-- `explorer %LOCALAPPDATA%\Ollama` to view logs
+- `explorer %LOCALAPPDATA%\Ollama` to view logs.  The most recent server logs will be in `server.log` and older logs will be in `server-#.log` 
 - `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
 - `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
@@ -37,16 +37,9 @@ Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
 
 ## LLM libraries
 
-Ollama includes multiple LLM libraries compiled for different GPUs and CPU
-vector features.  Ollama tries to pick the best one based on the capabilities of
-your system.  If this autodetection has problems, or you run into other problems
-(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
-library.  `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest
-but most compatible is `cpu`.  Rosetta emulation under MacOS will work with the
-`cpu` library. 
+Ollama includes multiple LLM libraries compiled for different GPUs and CPU vector features. Ollama tries to pick the best one based on the capabilities of your system. If this autodetection has problems, or you run into other problems (e.g. crashes in your GPU) you can workaround this by forcing a specific LLM library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest but most compatible is `cpu`. Rosetta emulation under MacOS will work with the `cpu` library. 
 
-In the server log, you will see a message that looks something like this (varies
-from release to release):
+In the server log, you will see a message that looks something like this (varies from release to release):
 
 ```
 Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
@@ -54,9 +47,7 @@ Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
 
 **Experimental LLM Library Override**
 
-You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass
-autodetection, so for example, if you have a CUDA card, but want to force the
-CPU LLM library with AVX2 vector support, use:
+You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
 
 ```
 OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
@@ -69,9 +60,7 @@ cat /proc/cpuinfo| grep flags  | head -1
 
 ## Installing older or pre-release versions on Linux
 
-If you run into problems on Linux and want to install an older version, or you'd
-like to try out a pre-release before it's officially released, you can tell the
-install script which version to install.
+If you run into problems on Linux and want to install an older version, or you'd like to try out a pre-release before it's officially released, you can tell the install script which version to install.
 
 ```sh
 curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
@@ -79,22 +68,20 @@ curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
 
 ## Linux tmp noexec 
 
-If your system is configured with the "noexec" flag where Ollama stores its
-temporary executable files, you can specify an alternate location by setting
-OLLAMA_TMPDIR to a location writable by the user ollama runs as.  For example
-OLLAMA_TMPDIR=/usr/share/ollama/
+If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
 
-## Container fails to run on NVIDIA GPU
+## NVIDIA GPU Discovery
 
-Make sure you've set up the conatiner runtime first as described in [docker.md](./docker.md)
+When Ollama starts up, it takes inventory of the GPUs present in the system to determine compatibility and how much VRAM is available.  Sometimes this discovery can fail to find your GPUs.  In general, running the latest driver will yield the best results.
 
-Sometimes the container runtime can have difficulties initializing the GPU.
-When you check the server logs, this can show up as various error codes, such
-as "3" (not initialized), "46" (device unavailable), "100" (no device), "999"
-(unknown), or others.  The following troubleshooting techniques may help resolve
-the problem
+### Linux NVIDIA Troubleshooting
 
-- Is the uvm driver not loaded? `sudo nvidia-modprobe -u`
+If you are using a container to run Ollama, make sure you've set up the container runtime first as described in [docker.md](./docker.md)
+
+Sometimes the Ollama can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
+
+- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama wont be able to see your NVIDIA GPU.
+- Is the uvm driver loaded? `sudo nvidia-modprobe -u`
 - Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
 - Try rebooting
 - Make sure you're running the latest nvidia drivers
@@ -102,3 +89,8 @@ the problem
 If none of those resolve the problem, gather additional information and file an issue:
 - Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
 - Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
+
+
+## Windows Terminal Errors
+
+Older versions of Windows 10 (e.g., 21H1) are known to have a bug where the standard terminal program does not display control characters correctly.  This can result in a long string of strings like `←[?25h←[?25l` being displayed, sometimes erroring with `The parameter is incorrect`  To resolve this problem, please update to Win 10 22H1 or newer.

docs/tutorials/langchainjs.md

@@ -15,7 +15,7 @@ import { Ollama } from "@langchain/community/llms/ollama";
 
 const ollama = new Ollama({
   baseUrl: "http://localhost:11434",
-  model: "llama3",
+  model: "llama3.1",
 });
 
 const answer = await ollama.invoke(`why is the sky blue?`);
@@ -23,7 +23,7 @@ const answer = await ollama.invoke(`why is the sky blue?`);
 console.log(answer);
 ```
 
-That will get us the same thing as if we ran `ollama run llama3 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
+That will get us the same thing as if we ran `ollama run llama3.1 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
 
 ```bash
 npm install cheerio

docs/tutorials/langchainpy.md

@@ -45,7 +45,7 @@ all_splits = text_splitter.split_documents(data)
 ```
 
 It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb`
-
+We also need to pull embedding model: `ollama pull nomic-embed-text`
 ```python
 from langchain.embeddings import OllamaEmbeddings
 from langchain.vectorstores import Chroma
@@ -68,7 +68,8 @@ The next thing is to send the question and the relevant parts of the docs to the
 ```python
 from langchain.chains import RetrievalQA
 qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
-qachain.invoke({"query": question})
+res = qachain.invoke({"query": question})
+print(res['result'])
 ```
 
 The answer received from this chain was:

docs/windows.md

@@ -19,10 +19,12 @@ Logs will often be helpful in diagnosing the problem (see
 
 ## System Requirements
 
-* Windows 10 or newer, Home or Pro
+* Windows 10 22H2 or newer, Home or Pro
 * NVIDIA 452.39 or newer Drivers if you have an NVIDIA card
 * AMD Radeon Driver https://www.amd.com/en/support if you have a Radeon card
 
+Ollama uses unicode characters for progress indication, which may render as unknown squares in some older terminal fonts in Windows 10. If you see this, try changing your terminal font settings.
+
 ## API Access
 
 Here's a quick example showing API access from `powershell`
@@ -33,14 +35,14 @@ Here's a quick example showing API access from `powershell`
 ## Troubleshooting
 
 While we're in preview, `OLLAMA_DEBUG` is always enabled, which adds
-a "view logs" menu item to the app, and increses logging for the GUI app and
+a "view logs" menu item to the app, and increases logging for the GUI app and
 server.
 
 Ollama on Windows stores files in a few different locations.  You can view them in
 the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
-    - *app.log* contains logs from the GUI application
-    - *server.log* contains the server logs
+    - *app.log* contains most resent logs from the GUI application
+    - *server.log* contains the most recent server logs
     - *upgrade.log* contains log output for upgrades
 - `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` contains models and configuration

envconfig/config.go

@@ -0,0 +1,284 @@
+package envconfig
+
+import (
+	"fmt"
+	"log/slog"
+	"math"
+	"net"
+	"net/url"
+	"os"
+	"path/filepath"
+	"runtime"
+	"strconv"
+	"strings"
+	"time"
+)
+
+// Host returns the scheme and host. Host can be configured via the OLLAMA_HOST environment variable.
+// Default is scheme "http" and host "127.0.0.1:11434"
+func Host() *url.URL {
+	defaultPort := "11434"
+
+	s := strings.TrimSpace(Var("OLLAMA_HOST"))
+	scheme, hostport, ok := strings.Cut(s, "://")
+	switch {
+	case !ok:
+		scheme, hostport = "http", s
+	case scheme == "http":
+		defaultPort = "80"
+	case scheme == "https":
+		defaultPort = "443"
+	}
+
+	// trim trailing slashes
+	hostport = strings.TrimRight(hostport, "/")
+
+	host, port, err := net.SplitHostPort(hostport)
+	if err != nil {
+		host, port = "127.0.0.1", defaultPort
+		if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
+			host = ip.String()
+		} else if hostport != "" {
+			host = hostport
+		}
+	}
+
+	if n, err := strconv.ParseInt(port, 10, 32); err != nil || n > 65535 || n < 0 {
+		slog.Warn("invalid port, using default", "port", port, "default", defaultPort)
+		return &url.URL{
+			Scheme: scheme,
+			Host:   net.JoinHostPort(host, defaultPort),
+		}
+	}
+
+	return &url.URL{
+		Scheme: scheme,
+		Host:   net.JoinHostPort(host, port),
+	}
+}
+
+// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
+func Origins() (origins []string) {
+	if s := Var("OLLAMA_ORIGINS"); s != "" {
+		origins = strings.Split(s, ",")
+	}
+
+	for _, origin := range []string{"localhost", "127.0.0.1", "0.0.0.0"} {
+		origins = append(origins,
+			fmt.Sprintf("http://%s", origin),
+			fmt.Sprintf("https://%s", origin),
+			fmt.Sprintf("http://%s", net.JoinHostPort(origin, "*")),
+			fmt.Sprintf("https://%s", net.JoinHostPort(origin, "*")),
+		)
+	}
+
+	origins = append(origins,
+		"app://*",
+		"file://*",
+		"tauri://*",
+	)
+
+	return origins
+}
+
+// Models returns the path to the models directory. Models directory can be configured via the OLLAMA_MODELS environment variable.
+// Default is $HOME/.ollama/models
+func Models() string {
+	if s := Var("OLLAMA_MODELS"); s != "" {
+		return s
+	}
+
+	home, err := os.UserHomeDir()
+	if err != nil {
+		panic(err)
+	}
+
+	return filepath.Join(home, ".ollama", "models")
+}
+
+// KeepAlive returns the duration that models stay loaded in memory. KeepAlive can be configured via the OLLAMA_KEEP_ALIVE environment variable.
+// Negative values are treated as infinite. Zero is treated as no keep alive.
+// Default is 5 minutes.
+func KeepAlive() (keepAlive time.Duration) {
+	keepAlive = 5 * time.Minute
+	if s := Var("OLLAMA_KEEP_ALIVE"); s != "" {
+		if d, err := time.ParseDuration(s); err == nil {
+			keepAlive = d
+		} else if n, err := strconv.ParseInt(s, 10, 64); err == nil {
+			keepAlive = time.Duration(n) * time.Second
+		}
+	}
+
+	if keepAlive < 0 {
+		return time.Duration(math.MaxInt64)
+	}
+
+	return keepAlive
+}
+
+func Bool(k string) func() bool {
+	return func() bool {
+		if s := Var(k); s != "" {
+			b, err := strconv.ParseBool(s)
+			if err != nil {
+				return true
+			}
+
+			return b
+		}
+
+		return false
+	}
+}
+
+var (
+	// Debug enabled additional debug information.
+	Debug = Bool("OLLAMA_DEBUG")
+	// FlashAttention enables the experimental flash attention feature.
+	FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
+	// NoHistory disables readline history.
+	NoHistory = Bool("OLLAMA_NOHISTORY")
+	// NoPrune disables pruning of model blobs on startup.
+	NoPrune = Bool("OLLAMA_NOPRUNE")
+	// SchedSpread allows scheduling models across all GPUs.
+	SchedSpread = Bool("OLLAMA_SCHED_SPREAD")
+	// IntelGPU enables experimental Intel GPU detection.
+	IntelGPU = Bool("OLLAMA_INTEL_GPU")
+)
+
+func String(s string) func() string {
+	return func() string {
+		return Var(s)
+	}
+}
+
+var (
+	LLMLibrary = String("OLLAMA_LLM_LIBRARY")
+	TmpDir     = String("OLLAMA_TMPDIR")
+
+	CudaVisibleDevices    = String("CUDA_VISIBLE_DEVICES")
+	HipVisibleDevices     = String("HIP_VISIBLE_DEVICES")
+	RocrVisibleDevices    = String("ROCR_VISIBLE_DEVICES")
+	GpuDeviceOrdinal      = String("GPU_DEVICE_ORDINAL")
+	HsaOverrideGfxVersion = String("HSA_OVERRIDE_GFX_VERSION")
+)
+
+func RunnersDir() (p string) {
+	if p := Var("OLLAMA_RUNNERS_DIR"); p != "" {
+		return p
+	}
+
+	if runtime.GOOS != "windows" {
+		return
+	}
+
+	defer func() {
+		if p == "" {
+			slog.Error("unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama_runners'")
+		}
+	}()
+
+	// On Windows we do not carry the payloads inside the main executable
+	exe, err := os.Executable()
+	if err != nil {
+		return
+	}
+
+	cwd, err := os.Getwd()
+	if err != nil {
+		return
+	}
+
+	var paths []string
+	for _, root := range []string{filepath.Dir(exe), cwd} {
+		paths = append(paths,
+			root,
+			filepath.Join(root, "windows-"+runtime.GOARCH),
+			filepath.Join(root, "dist", "windows-"+runtime.GOARCH),
+		)
+	}
+
+	// Try a few variations to improve developer experience when building from source in the local tree
+	for _, path := range paths {
+		candidate := filepath.Join(path, "ollama_runners")
+		if _, err := os.Stat(candidate); err == nil {
+			p = candidate
+			break
+		}
+	}
+
+	return p
+}
+
+func Uint(key string, defaultValue uint) func() uint {
+	return func() uint {
+		if s := Var(key); s != "" {
+			if n, err := strconv.ParseUint(s, 10, 64); err != nil {
+				slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
+			} else {
+				return uint(n)
+			}
+		}
+
+		return defaultValue
+	}
+}
+
+var (
+	// NumParallel sets the number of parallel model requests. NumParallel can be configured via the OLLAMA_NUM_PARALLEL environment variable.
+	NumParallel = Uint("OLLAMA_NUM_PARALLEL", 0)
+	// MaxRunners sets the maximum number of loaded models. MaxRunners can be configured via the OLLAMA_MAX_LOADED_MODELS environment variable.
+	MaxRunners = Uint("OLLAMA_MAX_LOADED_MODELS", 0)
+	// MaxQueue sets the maximum number of queued requests. MaxQueue can be configured via the OLLAMA_MAX_QUEUE environment variable.
+	MaxQueue = Uint("OLLAMA_MAX_QUEUE", 512)
+	// MaxVRAM sets a maximum VRAM override in bytes. MaxVRAM can be configured via the OLLAMA_MAX_VRAM environment variable.
+	MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
+)
+
+type EnvVar struct {
+	Name        string
+	Value       any
+	Description string
+}
+
+func AsMap() map[string]EnvVar {
+	ret := map[string]EnvVar{
+		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
+		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
+		"OLLAMA_HOST":              {"OLLAMA_HOST", Host(), "IP Address for the ollama server (default 127.0.0.1:11434)"},
+		"OLLAMA_KEEP_ALIVE":        {"OLLAMA_KEEP_ALIVE", KeepAlive(), "The duration that models stay loaded in memory (default \"5m\")"},
+		"OLLAMA_LLM_LIBRARY":       {"OLLAMA_LLM_LIBRARY", LLMLibrary(), "Set LLM library to bypass autodetection"},
+		"OLLAMA_MAX_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners(), "Maximum number of loaded models per GPU"},
+		"OLLAMA_MAX_QUEUE":         {"OLLAMA_MAX_QUEUE", MaxQueue(), "Maximum number of queued requests"},
+		"OLLAMA_MODELS":            {"OLLAMA_MODELS", Models(), "The path to the models directory"},
+		"OLLAMA_NOHISTORY":         {"OLLAMA_NOHISTORY", NoHistory(), "Do not preserve readline history"},
+		"OLLAMA_NOPRUNE":           {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
+		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
+		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
+		"OLLAMA_RUNNERS_DIR":       {"OLLAMA_RUNNERS_DIR", RunnersDir(), "Location for runners"},
+		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
+		"OLLAMA_TMPDIR":            {"OLLAMA_TMPDIR", TmpDir(), "Location for temporary files"},
+	}
+	if runtime.GOOS != "darwin" {
+		ret["CUDA_VISIBLE_DEVICES"] = EnvVar{"CUDA_VISIBLE_DEVICES", CudaVisibleDevices(), "Set which NVIDIA devices are visible"}
+		ret["HIP_VISIBLE_DEVICES"] = EnvVar{"HIP_VISIBLE_DEVICES", HipVisibleDevices(), "Set which AMD devices are visible"}
+		ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices(), "Set which AMD devices are visible"}
+		ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal(), "Set which AMD devices are visible"}
+		ret["HSA_OVERRIDE_GFX_VERSION"] = EnvVar{"HSA_OVERRIDE_GFX_VERSION", HsaOverrideGfxVersion(), "Override the gfx used for all detected AMD GPUs"}
+		ret["OLLAMA_INTEL_GPU"] = EnvVar{"OLLAMA_INTEL_GPU", IntelGPU(), "Enable experimental Intel GPU detection"}
+	}
+	return ret
+}
+
+func Values() map[string]string {
+	vals := make(map[string]string)
+	for k, v := range AsMap() {
+		vals[k] = fmt.Sprintf("%v", v.Value)
+	}
+	return vals
+}
+
+// Var returns an environment variable stripped of leading and trailing quotes or spaces
+func Var(key string) string {
+	return strings.Trim(strings.TrimSpace(os.Getenv(key)), "\"'")
+}

envconfig/config_test.go

@@ -0,0 +1,235 @@
+package envconfig
+
+import (
+	"math"
+	"testing"
+	"time"
+
+	"github.com/google/go-cmp/cmp"
+)
+
+func TestHost(t *testing.T) {
+	cases := map[string]struct {
+		value  string
+		expect string
+	}{
+		"empty":               {"", "127.0.0.1:11434"},
+		"only address":        {"1.2.3.4", "1.2.3.4:11434"},
+		"only port":           {":1234", ":1234"},
+		"address and port":    {"1.2.3.4:1234", "1.2.3.4:1234"},
+		"hostname":            {"example.com", "example.com:11434"},
+		"hostname and port":   {"example.com:1234", "example.com:1234"},
+		"zero port":           {":0", ":0"},
+		"too large port":      {":66000", ":11434"},
+		"too small port":      {":-1", ":11434"},
+		"ipv6 localhost":      {"[::1]", "[::1]:11434"},
+		"ipv6 world open":     {"[::]", "[::]:11434"},
+		"ipv6 no brackets":    {"::1", "[::1]:11434"},
+		"ipv6 + port":         {"[::1]:1337", "[::1]:1337"},
+		"extra space":         {" 1.2.3.4 ", "1.2.3.4:11434"},
+		"extra quotes":        {"\"1.2.3.4\"", "1.2.3.4:11434"},
+		"extra space+quotes":  {" \" 1.2.3.4 \" ", "1.2.3.4:11434"},
+		"extra single quotes": {"'1.2.3.4'", "1.2.3.4:11434"},
+		"http":                {"http://1.2.3.4", "1.2.3.4:80"},
+		"http port":           {"http://1.2.3.4:4321", "1.2.3.4:4321"},
+		"https":               {"https://1.2.3.4", "1.2.3.4:443"},
+		"https port":          {"https://1.2.3.4:4321", "1.2.3.4:4321"},
+	}
+
+	for name, tt := range cases {
+		t.Run(name, func(t *testing.T) {
+			t.Setenv("OLLAMA_HOST", tt.value)
+			if host := Host(); host.Host != tt.expect {
+				t.Errorf("%s: expected %s, got %s", name, tt.expect, host.Host)
+			}
+		})
+	}
+}
+
+func TestOrigins(t *testing.T) {
+	cases := []struct {
+		value  string
+		expect []string
+	}{
+		{"", []string{
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+		{"http://10.0.0.1", []string{
+			"http://10.0.0.1",
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+		{"http://172.16.0.1,https://192.168.0.1", []string{
+			"http://172.16.0.1",
+			"https://192.168.0.1",
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+		{"http://totally.safe,http://definitely.legit", []string{
+			"http://totally.safe",
+			"http://definitely.legit",
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+	}
+	for _, tt := range cases {
+		t.Run(tt.value, func(t *testing.T) {
+			t.Setenv("OLLAMA_ORIGINS", tt.value)
+
+			if diff := cmp.Diff(Origins(), tt.expect); diff != "" {
+				t.Errorf("%s: mismatch (-want +got):\n%s", tt.value, diff)
+			}
+		})
+	}
+}
+
+func TestBool(t *testing.T) {
+	cases := map[string]bool{
+		"":      false,
+		"true":  true,
+		"false": false,
+		"1":     true,
+		"0":     false,
+		// invalid values
+		"random":    true,
+		"something": true,
+	}
+
+	for k, v := range cases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_BOOL", k)
+			if b := Bool("OLLAMA_BOOL")(); b != v {
+				t.Errorf("%s: expected %t, got %t", k, v, b)
+			}
+		})
+	}
+}
+
+func TestUint(t *testing.T) {
+	cases := map[string]uint{
+		"0":    0,
+		"1":    1,
+		"1337": 1337,
+		// default values
+		"":       11434,
+		"-1":     11434,
+		"0o10":   11434,
+		"0x10":   11434,
+		"string": 11434,
+	}
+
+	for k, v := range cases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_UINT", k)
+			if i := Uint("OLLAMA_UINT", 11434)(); i != v {
+				t.Errorf("%s: expected %d, got %d", k, v, i)
+			}
+		})
+	}
+}
+
+func TestKeepAlive(t *testing.T) {
+	cases := map[string]time.Duration{
+		"":       5 * time.Minute,
+		"1s":     time.Second,
+		"1m":     time.Minute,
+		"1h":     time.Hour,
+		"5m0s":   5 * time.Minute,
+		"1h2m3s": 1*time.Hour + 2*time.Minute + 3*time.Second,
+		"0":      time.Duration(0),
+		"60":     60 * time.Second,
+		"120":    2 * time.Minute,
+		"3600":   time.Hour,
+		"-0":     time.Duration(0),
+		"-1":     time.Duration(math.MaxInt64),
+		"-1m":    time.Duration(math.MaxInt64),
+		// invalid values
+		" ":   5 * time.Minute,
+		"???": 5 * time.Minute,
+		"1d":  5 * time.Minute,
+		"1y":  5 * time.Minute,
+		"1w":  5 * time.Minute,
+	}
+
+	for tt, expect := range cases {
+		t.Run(tt, func(t *testing.T) {
+			t.Setenv("OLLAMA_KEEP_ALIVE", tt)
+			if actual := KeepAlive(); actual != expect {
+				t.Errorf("%s: expected %s, got %s", tt, expect, actual)
+			}
+		})
+	}
+}
+
+func TestVar(t *testing.T) {
+	cases := map[string]string{
+		"value":       "value",
+		" value ":     "value",
+		" 'value' ":   "value",
+		` "value" `:   "value",
+		" ' value ' ": " value ",
+		` " value " `: " value ",
+	}
+
+	for k, v := range cases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_VAR", k)
+			if s := Var("OLLAMA_VAR"); s != v {
+				t.Errorf("%s: expected %q, got %q", k, v, s)
+			}
+		})
+	}
+}

examples/go-chat/main.go

@@ -35,7 +35,7 @@ func main() {
 
 	ctx := context.Background()
 	req := &api.ChatRequest{
-		Model:    "llama3",
+		Model:    "llama3.1",
 		Messages: messages,
 	}
 

examples/go-generate-streaming/main.go

@@ -16,7 +16,7 @@ func main() {
 
 	// By default, GenerateRequest is streaming.
 	req := &api.GenerateRequest{
-		Model:  "gemma",
+		Model:  "gemma2",
 		Prompt: "how many planets are there?",
 	}
 

examples/go-generate/main.go

@@ -15,7 +15,7 @@ func main() {
 	}
 
 	req := &api.GenerateRequest{
-		Model:  "gemma",
+		Model:  "gemma2",
 		Prompt: "how many planets are there?",
 
 		// set streaming to false

examples/langchain-python-rag-document/README.md

@@ -4,6 +4,14 @@ This example provides an interface for asking questions to a PDF document.
 
 ## Setup
 
+1. Ensure you have the `llama3.1` model installed:
+
+```
+ollama pull llama3.1
+```
+
+2. Install the Python Requirements.
+
 ```
 pip install -r requirements.txt
 ```

examples/langchain-python-rag-document/main.py

@@ -51,7 +51,7 @@ while True:
         template=template,
     )
 
-    llm = Ollama(model="llama3:8b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
+    llm = Ollama(model="llama3.1", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
     qa_chain = RetrievalQA.from_chain_type(
         llm,
         retriever=vectorstore.as_retriever(),

examples/langchain-python-rag-privategpt/ingest.py

@@ -77,13 +77,21 @@ LOADER_MAPPING = {
 
 
 def load_single_document(file_path: str) -> List[Document]:
-    ext = "." + file_path.rsplit(".", 1)[-1]
+    if os.path.getsize(file_path) != 0:
+        filename, ext = os.path.splitext(file_path)
         if ext in LOADER_MAPPING:
             loader_class, loader_args = LOADER_MAPPING[ext]
+            try:
                 loader = loader_class(file_path, **loader_args)
+                if loader:
                     return loader.load()
+            except:
+                print(f"Corrupted file {file_path}. Ignoring it.")
+        else:
+            print(f"Unsupported file {file_path}. Ignoring it.")
+    else:
+        print(f"Empty file {file_path}. Ignoring it.")
 
-    raise ValueError(f"Unsupported file extension '{ext}'")
 
 def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
     """
@@ -100,6 +108,7 @@ def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Docum
         results = []
         with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
             for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
+                if docs:
                     results.extend(docs)
                 pbar.update()
 

examples/langchain-python-rag-privategpt/requirements.txt

@@ -12,3 +12,4 @@ pandoc==2.3
 pypandoc==1.11
 tqdm==4.66.1
 sentence_transformers==2.2.2
+numpy>=1.22.2 # not directly required, pinned by Snyk to avoid a vulnerability

examples/langchain-python-rag-websummary/README.md

@@ -4,10 +4,10 @@ This example summarizes the website, [https://ollama.com/blog/run-llama2-uncenso
 
 ## Running the Example
 
-1. Ensure you have the `llama2` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama2
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/langchain-python-rag-websummary/main.py

@@ -5,7 +5,7 @@ from langchain.chains.summarize import load_summarize_chain
 loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
 docs = loader.load()
 
-llm = Ollama(model="llama3")
+llm = Ollama(model="llama3.1")
 chain = load_summarize_chain(llm, chain_type="stuff")
 
 result = chain.invoke(docs)

examples/langchain-python-simple/README.md

@@ -4,10 +4,10 @@ This example is a basic "hello world" of using LangChain with Ollama.
 
 ## Running the Example
 
-1. Ensure you have the `llama3` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/langchain-python-simple/main.py

@@ -1,6 +1,6 @@
 from langchain.llms import Ollama
 
 input = input("What is your question?")
-llm = Ollama(model="llama3")
+llm = Ollama(model="llama3.1")
 res = llm.predict(input)
 print (res)

examples/modelfile-mario/Modelfile

@@ -1,4 +1,4 @@
-FROM llama3
+FROM llama3.1
 PARAMETER temperature 1
 SYSTEM """
 You are Mario from super mario bros, acting as an assistant.

examples/modelfile-mario/readme.md

@@ -2,12 +2,12 @@
 
 # Example character: Mario
 
-This example shows how to create a basic character using Llama3 as the base model.
+This example shows how to create a basic character using Llama3.1 as the base model.
 
 To run this example:
 
 1. Download the Modelfile
-2. `ollama pull llama3` to get the base model used in the model file.
+2. `ollama pull llama3.1` to get the base model used in the model file.
 3. `ollama create NAME -f ./Modelfile`
 4. `ollama run NAME`
 
@@ -18,7 +18,7 @@ Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
 What the model file looks like:
 
 ```
-FROM llama3
+FROM llama3.1
 PARAMETER temperature 1
 SYSTEM """
 You are Mario from Super Mario Bros, acting as an assistant.

examples/python-dockerit/dockerit.py

@@ -4,7 +4,7 @@ imageName = input("Enter the name of the image: ")
 client = docker.from_env()
 s = requests.Session()
 output=""
-with s.post('http://localhost:11434/api/generate', json={'model': 'dockerit', 'prompt': inputDescription}, stream=True) as r:
+with s.post('http://localhost:11434/api/generate', json={'model': 'mattw/dockerit', 'prompt': inputDescription}, stream=True) as r:
   for line in r.iter_lines():
     if line:
       j = json.loads(line)

examples/python-json-datagenerator/predefinedschema.py

@@ -2,7 +2,7 @@ import requests
 import json
 import random
 
-model = "llama3"
+model = "llama3.1"
 template = {
   "firstName": "",
   "lastName": "",

examples/python-json-datagenerator/randomaddresses.py

@@ -12,7 +12,7 @@ countries = [
     "France",
 ]
 country = random.choice(countries)
-model = "llama3"
+model = "llama3.1"
 
 prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
 

examples/python-json-datagenerator/readme.md

@@ -6,10 +6,10 @@ There are two python scripts in this example. `randomaddresses.py` generates ran
 
 ## Running the Example
 
-1. Ensure you have the `llama3` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/python-simplechat/client.py

@@ -2,13 +2,14 @@ import json
 import requests
 
 # NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-model = "llama3"  # TODO: update this for whatever model you wish to use
+model = "llama3.1"  # TODO: update this for whatever model you wish to use
 
 
 def chat(messages):
     r = requests.post(
         "http://0.0.0.0:11434/api/chat",
         json={"model": model, "messages": messages, "stream": True},
+	stream=True
     )
     r.raise_for_status()
     output = ""

examples/python-simplechat/readme.md

@@ -4,10 +4,10 @@ The **chat** endpoint is one of two ways to generate text from an LLM with Ollam
 
 ## Running the Example
 
-1. Ensure you have the `llama3` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/typescript-simplechat/client.ts

@@ -1,6 +1,6 @@
 import * as readline from "readline";
 
-const model = "llama3";
+const model = "llama3.1";
 type Message = {
   role: "assistant" | "user" | "system";
   content: string;

format/format_test.go

@@ -5,7 +5,6 @@ import (
 )
 
 func TestHumanNumber(t *testing.T) {
-
 	type testCase struct {
 		input    uint64
 		expected string

format/time.go

@@ -60,7 +60,9 @@ func humanTime(t time.Time, zeroValue string) string {
 	}
 
 	delta := time.Since(t)
-	if delta < 0 {
+	if int(delta.Hours())/24/365 < -20 {
+		return "Forever"
+	} else if delta < 0 {
 		return humanDuration(-delta) + " from now"
 	}
 

format/time_test.go

@@ -32,4 +32,14 @@ func TestHumanTime(t *testing.T) {
 		v := now.Add(800 * time.Millisecond)
 		assertEqual(t, HumanTime(v, ""), "Less than a second from now")
 	})
+
+	t.Run("time way in the future", func(t *testing.T) {
+		v := now.Add(24 * time.Hour * 365 * 200)
+		assertEqual(t, HumanTime(v, ""), "Forever")
+	})
+
+	t.Run("time way in the future lowercase", func(t *testing.T) {
+		v := now.Add(24 * time.Hour * 365 * 200)
+		assertEqual(t, HumanTimeLower(v, ""), "forever")
+	})
 }

go.mod

@@ -1,77 +1,78 @@
 module github.com/ollama/ollama
 
-go 1.22
-
-toolchain go1.22.0
+go 1.22.0
 
 require (
 	github.com/containerd/console v1.0.3
-	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
 	github.com/emirpasic/gods v1.18.1
-	github.com/gin-gonic/gin v1.9.1
-	github.com/golang/protobuf v1.5.0 // indirect
-	github.com/google/uuid v1.0.0
-	github.com/mitchellh/mapstructure v1.5.0
+	github.com/gin-gonic/gin v1.10.0
+	github.com/golang/protobuf v1.5.4 // indirect
+	github.com/google/uuid v1.1.2
 	github.com/olekukonko/tablewriter v0.0.5
 	github.com/spf13/cobra v1.7.0
-	github.com/stretchr/testify v1.8.4
+	github.com/stretchr/testify v1.9.0
 	github.com/x448/float16 v0.8.4
 	golang.org/x/sync v0.3.0
 )
 
 require (
+	github.com/agnivade/levenshtein v1.1.1
+	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
+	github.com/google/go-cmp v0.6.0
+	github.com/mattn/go-runewidth v0.0.14
 	github.com/nlpodyssey/gopickle v0.3.0
-	github.com/pdevine/tensor v0.0.0-20240228013915-64ccaa8d9ca9
+	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
 )
 
 require (
-	github.com/apache/arrow/go/arrow v0.0.0-20201229220542-30ce2eb5d4dc // indirect
+	github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 // indirect
+	github.com/bytedance/sonic/loader v0.1.1 // indirect
 	github.com/chewxy/hm v1.0.0 // indirect
-	github.com/chewxy/math32 v1.0.8 // indirect
+	github.com/chewxy/math32 v1.10.1 // indirect
+	github.com/cloudwego/base64x v0.1.4 // indirect
+	github.com/cloudwego/iasm v0.2.0 // indirect
 	github.com/davecgh/go-spew v1.1.1 // indirect
 	github.com/gogo/protobuf v1.3.2 // indirect
-	github.com/google/flatbuffers v1.12.0 // indirect
-	github.com/mattn/go-runewidth v0.0.14 // indirect
+	github.com/google/flatbuffers v24.3.25+incompatible // indirect
+	github.com/kr/text v0.2.0 // indirect
 	github.com/pkg/errors v0.9.1 // indirect
 	github.com/pmezard/go-difflib v1.0.0 // indirect
 	github.com/rivo/uniseg v0.2.0 // indirect
 	github.com/xtgo/set v1.0.0 // indirect
 	go4.org/unsafe/assume-no-moving-gc v0.0.0-20231121144256-b99613f794b6 // indirect
 	golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 // indirect
-	gonum.org/v1/gonum v0.8.2 // indirect
+	gonum.org/v1/gonum v0.15.0 // indirect
 	gorgonia.org/vecf32 v0.9.0 // indirect
 	gorgonia.org/vecf64 v0.9.0 // indirect
 )
 
 require (
-	github.com/bytedance/sonic v1.9.1 // indirect
-	github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 // indirect
-	github.com/gabriel-vasile/mimetype v1.4.2 // indirect
-	github.com/gin-contrib/cors v1.4.0
+	github.com/bytedance/sonic v1.11.6 // indirect
+	github.com/gabriel-vasile/mimetype v1.4.3 // indirect
+	github.com/gin-contrib/cors v1.7.2
 	github.com/gin-contrib/sse v0.1.0 // indirect
 	github.com/go-playground/locales v0.14.1 // indirect
 	github.com/go-playground/universal-translator v0.18.1 // indirect
-	github.com/go-playground/validator/v10 v10.14.0 // indirect
+	github.com/go-playground/validator/v10 v10.20.0 // indirect
 	github.com/goccy/go-json v0.10.2 // indirect
-	github.com/google/go-cmp v0.5.9 // indirect
 	github.com/inconshreveable/mousetrap v1.1.0 // indirect
 	github.com/json-iterator/go v1.1.12 // indirect
-	github.com/klauspost/cpuid/v2 v2.2.4 // indirect
-	github.com/leodido/go-urn v1.2.4 // indirect
-	github.com/mattn/go-isatty v0.0.19 // indirect
+	github.com/klauspost/cpuid/v2 v2.2.7 // indirect
+	github.com/leodido/go-urn v1.4.0 // indirect
+	github.com/mattn/go-isatty v0.0.20 // indirect
 	github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
 	github.com/modern-go/reflect2 v1.0.2 // indirect
-	github.com/pelletier/go-toml/v2 v2.0.8 // indirect
+	github.com/pelletier/go-toml/v2 v2.2.2 // indirect
 	github.com/spf13/pflag v1.0.5 // indirect
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
-	github.com/ugorji/go/codec v1.2.11 // indirect
-	golang.org/x/arch v0.3.0 // indirect
-	golang.org/x/crypto v0.14.0
-	golang.org/x/exp v0.0.0-20230817173708-d852ddb80c63
-	golang.org/x/net v0.17.0 // indirect
-	golang.org/x/sys v0.13.0
-	golang.org/x/term v0.13.0
-	golang.org/x/text v0.14.0 // indirect
-	google.golang.org/protobuf v1.30.0
+	github.com/ugorji/go/codec v1.2.12 // indirect
+	golang.org/x/arch v0.8.0 // indirect
+	golang.org/x/crypto v0.23.0
+	golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa
+	golang.org/x/net v0.25.0 // indirect
+	golang.org/x/sys v0.20.0
+	golang.org/x/term v0.20.0
+	golang.org/x/text v0.15.0
+	google.golang.org/protobuf v1.34.1
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -1,22 +1,36 @@
 cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU=
+gioui.org v0.0.0-20210308172011-57750fc8a0a6/go.mod h1:RSH6KIUZ0p2xy5zHDxgAM4zumjgTw83q2ge/PI+yyw8=
 github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU=
+github.com/BurntSushi/xgb v0.0.0-20160522181843-27f122750802/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo=
+github.com/agnivade/levenshtein v1.1.1 h1:QY8M92nrzkmr798gCo3kmMyqXFzdQVpxLlGPRBij0P8=
+github.com/agnivade/levenshtein v1.1.1/go.mod h1:veldBMzWxcCG2ZvUTKD2kJNRdCk5hVbJomOvKkmgYbo=
 github.com/ajstarks/svgo v0.0.0-20180226025133-644b8db467af/go.mod h1:K08gAheRH3/J6wwsYMMT4xOr94bZjxIelGM0+d/wbFw=
-github.com/apache/arrow/go/arrow v0.0.0-20201229220542-30ce2eb5d4dc h1:zvQ6w7KwtQWgMQiewOF9tFtundRMVZFSAksNV6ogzuY=
-github.com/apache/arrow/go/arrow v0.0.0-20201229220542-30ce2eb5d4dc/go.mod h1:c9sxoIT3YgLxH4UhLOCKaBlEojuMhVYpk4Ntv3opUTQ=
-github.com/bytedance/sonic v1.5.0/go.mod h1:ED5hyg4y6t3/9Ku1R6dU/4KyJ48DZ4jPhfY1O2AihPM=
-github.com/bytedance/sonic v1.9.1 h1:6iJ6NqdoxCDr6mbY8h18oSO+cShGSMRGCEo7F2h0x8s=
-github.com/bytedance/sonic v1.9.1/go.mod h1:i736AoUSYt75HyZLoJW9ERYxcy6eaN6h4BZXU064P/U=
+github.com/antihax/optional v1.0.0/go.mod h1:uupD/76wgC+ih3iEmQUL+0Ugr19nfwCT1kdvxnR2qWY=
+github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 h1:q4dksr6ICHXqG5hm0ZW5IHyeEJXoIJSOZeBLmWPNeIQ=
+github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40/go.mod h1:Q7yQnSMnLvcXlZ8RV+jwz/6y1rQTqbX6C82SndT52Zs=
+github.com/arbovm/levenshtein v0.0.0-20160628152529-48b4e1c0c4d0 h1:jfIu9sQUG6Ig+0+Ap1h4unLjW6YQJpKZVmUzxsD4E/Q=
+github.com/arbovm/levenshtein v0.0.0-20160628152529-48b4e1c0c4d0/go.mod h1:t2tdKJDJF9BV14lnkjHmOQgcvEKgtqs5a1N3LNdJhGE=
+github.com/boombuler/barcode v1.0.0/go.mod h1:paBWMcWSl3LHKBqUq+rly7CNSldXjb2rDl3JlRe0mD8=
+github.com/bytedance/sonic v1.11.6 h1:oUp34TzMlL+OY1OUWxHqsdkgC/Zfc85zGqw9siXjrc0=
+github.com/bytedance/sonic v1.11.6/go.mod h1:LysEHSvpvDySVdC2f87zGWf6CIKJcAvqab1ZaiQtds4=
+github.com/bytedance/sonic/loader v0.1.1 h1:c+e5Pt1k/cy5wMveRDyk2X4B9hF4g7an8N3zCYjJFNM=
+github.com/bytedance/sonic/loader v0.1.1/go.mod h1:ncP89zfokxS5LZrJxl5z0UJcsk4M4yY2JpfqGeCtNLU=
 github.com/census-instrumentation/opencensus-proto v0.2.1/go.mod h1:f6KPmirojxKA12rnyqOA5BBL4O983OfeGPqjHWSTneU=
-github.com/chenzhuoyu/base64x v0.0.0-20211019084208-fb5309c8db06/go.mod h1:DH46F32mSOjUmXrMHnKwZdA8wcEefY7UVqBKYGjpdQY=
-github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 h1:qSGYFH7+jGhDF8vLC+iwCD4WpbV1EBDSzWkJODFLams=
-github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311/go.mod h1:b583jCggY9gE99b6G5LEC39OIiVsWj+R97kbl5odCEk=
 github.com/chewxy/hm v1.0.0 h1:zy/TSv3LV2nD3dwUEQL2VhXeoXbb9QkpmdRAVUFiA6k=
 github.com/chewxy/hm v1.0.0/go.mod h1:qg9YI4q6Fkj/whwHR1D+bOGeF7SniIP40VweVepLjg0=
 github.com/chewxy/math32 v1.0.0/go.mod h1:Miac6hA1ohdDUTagnvJy/q+aNnEk16qWUdb8ZVhvCN0=
-github.com/chewxy/math32 v1.0.8 h1:fU5E4Ec4Z+5RtRAi3TovSxUjQPkgRh+HbP7tKB2OFbM=
-github.com/chewxy/math32 v1.0.8/go.mod h1:dOB2rcuFrCn6UHrze36WSLVPKtzPMRAQvBvUwkSsLqs=
+github.com/chewxy/math32 v1.10.1 h1:LFpeY0SLJXeaiej/eIp2L40VYfscTvKh/FSEZ68uMkU=
+github.com/chewxy/math32 v1.10.1/go.mod h1:dOB2rcuFrCn6UHrze36WSLVPKtzPMRAQvBvUwkSsLqs=
 github.com/client9/misspell v0.3.4/go.mod h1:qj6jICC3Q7zFZvVWo7KLAzC3yx5G7kyvSDkc90ppPyw=
+github.com/cloudwego/base64x v0.1.4 h1:jwCgWpFanWmN8xoIUHa2rtzmkd5J2plF/dnLS6Xd/0Y=
+github.com/cloudwego/base64x v0.1.4/go.mod h1:0zlkT4Wn5C6NdauXdJRhSKRlJvmclQ1hhJgA0rcu/8w=
+github.com/cloudwego/iasm v0.2.0 h1:1KNIy1I1H9hNNFEEH3DVnI4UujN+1zjpuk6gwHLTssg=
+github.com/cloudwego/iasm v0.2.0/go.mod h1:8rXZaNYT2n95jn+zTI1sDr+IgcD2GVs0nlbbQPiEFhY=
 github.com/cncf/udpa/go v0.0.0-20191209042840-269d4d468f6f/go.mod h1:M8M6+tZqaGXZJjfX53e64911xZQV5JYwmTeXPW+k8Sc=
+github.com/cncf/udpa/go v0.0.0-20201120205902-5459f2c99403/go.mod h1:WmhPx2Nbnhtbo57+VJT5O0JRkEi1Wbu0z5j0R8u5Hbk=
+github.com/cncf/xds/go v0.0.0-20210312221358-fbca930ec8ed/go.mod h1:eXthEFrGJvWHgFFCl3hGmgk+/aYT6PnTQLykKQRLhEs=
 github.com/containerd/console v1.0.3 h1:lIr7SlA5PxZyMV30bDW0MGbiOPXwc63yRuCP0ARubLw=
 github.com/containerd/console v1.0.3/go.mod h1:7LqA/THxQ86k76b8c/EMSiaJ3h1eZkMkXar0TQ1gf3U=
 github.com/cpuguy83/go-md2man/v2 v2.0.2/go.mod h1:tgQtvFlXSQOSOSIRvRPT7W67SCa46tRHOmNcaadrF8o=
@@ -26,35 +40,42 @@ github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1/go.mod h1:uw2gLc
 github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
 github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
 github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48 h1:fRzb/w+pyskVMQ+UbP35JkH8yB7MYb4q/qhBarqZE6g=
+github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48/go.mod h1:if7Fbed8SFyPtHLHbg49SI7NAdJiC5WIA09pe59rfAA=
 github.com/emirpasic/gods v1.18.1 h1:FXtiHYKDGKCW2KzwZKx0iC0PQmdlorYgdFG9jPXJ1Bc=
 github.com/emirpasic/gods v1.18.1/go.mod h1:8tpGGwCnJ5H4r6BWwaV6OrWmMoPhUl5jm/FMNAnJvWQ=
 github.com/envoyproxy/go-control-plane v0.9.0/go.mod h1:YTl/9mNaCwkRvm6d1a2C3ymFceY/DCBVvsKhRF0iEA4=
 github.com/envoyproxy/go-control-plane v0.9.1-0.20191026205805-5f8ba28d4473/go.mod h1:YTl/9mNaCwkRvm6d1a2C3ymFceY/DCBVvsKhRF0iEA4=
 github.com/envoyproxy/go-control-plane v0.9.4/go.mod h1:6rpuAdCZL397s3pYoYcLgu1mIlRU8Am5FuJP05cCM98=
+github.com/envoyproxy/go-control-plane v0.9.9-0.20201210154907-fd9021fe5dad/go.mod h1:cXg6YxExXjJnVBQHBLXeUAgxn2UodCpnH306RInaBQk=
+github.com/envoyproxy/go-control-plane v0.9.9-0.20210217033140-668b12f5399d/go.mod h1:cXg6YxExXjJnVBQHBLXeUAgxn2UodCpnH306RInaBQk=
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-golang.org/x/text v0.14.0 h1:ScX5w1eTa3QqT8oi6+ziP7dTV1S2+ALU0bI+0zXKWiQ=
-golang.org/x/text v0.14.0/go.mod h1:18ZOQIKpY8NJVqYksKHtTdi31H5itFRjB5/qKTNYzSU=
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@@ -247,34 +298,40 @@ golang.org/x/tools v0.0.0-20190206041539-40960b6deb8e/go.mod h1:n7NCudcB/nEzxVGm
 golang.org/x/tools v0.0.0-20190226205152-f727befe758c/go.mod h1:9Yl7xja0Znq3iFh3HoIrodX9oNMXvdceNzlUR8zjMvY=
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+google.golang.org/grpc v1.39.0/go.mod h1:PImNr+rS9TWYb2O4/emRugxiyHZ5JyHW5F+RPnDzfrE=
 google.golang.org/protobuf v0.0.0-20200109180630-ec00e32a8dfd/go.mod h1:DFci5gLYBciE7Vtevhsrf46CRTquxDuWsQurQQe4oz8=
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@@ -283,20 +340,18 @@ google.golang.org/protobuf v1.21.0/go.mod h1:47Nbq4nVaFHyn7ilMalzfO3qCViNmqZ2kzi
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 google.golang.org/protobuf v1.23.1-0.20200526195155-81db48ad09cc/go.mod h1:EGpADcykh3NcUnDUJcl1+ZksZNG86OlYog2l/sGQquU=
-google.golang.org/protobuf v1.24.0/go.mod h1:r/3tXBNzIEhYS9I1OUVjXDlt8tc493IdKGjtUeSXeh4=
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 google.golang.org/protobuf v1.26.0-rc.1/go.mod h1:jlhhOSvTdKEhbULTjvd4ARK9grFBp09yW+WbY/TyQbw=
-google.golang.org/protobuf v1.28.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
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+google.golang.org/protobuf v1.34.1 h1:9ddQBjfCyZPOHPUiPxpYESBLc+T8P3E+Vo4IbKZgFWg=
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-gopkg.in/errgo.v2 v2.1.0/go.mod h1:hNsd1EY+bozCKY1Ytp96fpM3vjJbqLJn88ws8XvfDNI=
-gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
+gopkg.in/yaml.v2 v2.2.2/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
+gopkg.in/yaml.v2 v2.2.3/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
 gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
-gopkg.in/yaml.v3 v3.0.0-20210107192922-496545a6307b/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
 gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gorgonia.org/vecf32 v0.9.0 h1:PClazic1r+JVJ1dEzRXgeiVl4g1/Hf/w+wUSqnco1Xg=
@@ -305,4 +360,5 @@ gorgonia.org/vecf64 v0.9.0 h1:bgZDP5x0OzBF64PjMGC3EvTdOoMEcmfAh1VCUnZFm1A=
 gorgonia.org/vecf64 v0.9.0/go.mod h1:hp7IOWCnRiVQKON73kkC/AUMtEXyf9kGlVrtPQ9ccVA=
 honnef.co/go/tools v0.0.0-20190102054323-c2f93a96b099/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
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+nullprogram.com/x/optparse v1.0.0/go.mod h1:KdyPE+Igbe0jQUrVfMqDMeJQIJZEuyV7pjYmp6pbG50=
 rsc.io/pdf v0.1.1/go.mod h1:n8OzWcQ6Sp37PL01nO98y4iUCRdTGarVfzxY20ICaU4=

gpu/amd_common.go

@@ -49,9 +49,17 @@ func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
 }
 
 func commonAMDValidateLibDir() (string, error) {
-	// We try to favor system paths first, so that we can wire up the subprocess to use
-	// the system version.  Only use our bundled version if the system version doesn't work
-	// This gives users a more recovery options if versions have subtle problems at runtime
+	// Favor our bundled version
+
+	// Installer payload location if we're running the installed binary
+	exe, err := os.Executable()
+	if err == nil {
+		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
+		if rocmLibUsable(rocmTargetDir) {
+			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
+			return rocmTargetDir, nil
+		}
+	}
 
 	// Prefer explicit HIP env var
 	hipPath := os.Getenv("HIP_PATH")
@@ -87,14 +95,5 @@ func commonAMDValidateLibDir() (string, error) {
 		}
 	}
 
-	// Installer payload location if we're running the installed binary
-	exe, err := os.Executable()
-	if err == nil {
-		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
-		if rocmLibUsable(rocmTargetDir) {
-			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
-			return rocmTargetDir, nil
-		}
-	}
 	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }

gpu/amd_hip_windows.go

@@ -33,9 +33,10 @@ type HipLib struct {
 }
 
 func NewHipLib() (*HipLib, error) {
-	h, err := windows.LoadLibrary("amdhip64.dll")
+	// At runtime we depend on v6, so discover GPUs with the same library for a consistent set of GPUs
+	h, err := windows.LoadLibrary("amdhip64_6.dll")
 	if err != nil {
-		return nil, fmt.Errorf("unable to load amdhip64.dll: %w", err)
+		return nil, fmt.Errorf("unable to load amdhip64_6.dll, please make sure to upgrade to the latest amd driver: %w", err)
 	}
 	hl := &HipLib{}
 	hl.dll = h
@@ -84,9 +85,8 @@ func (hl *HipLib) AMDDriverVersion() (driverMajor, driverMinor int, err error) {
 	}
 
 	slog.Debug("hipDriverGetVersion", "version", version)
-	// TODO - this isn't actually right, but the docs claim hipDriverGetVersion isn't accurate anyway...
-	driverMajor = version / 1000
-	driverMinor = (version - (driverMajor * 1000)) / 10
+	driverMajor = version / 10000000
+	driverMinor = (version - (driverMajor * 10000000)) / 100000
 
 	return driverMajor, driverMinor, nil
 }

gpu/amd_linux.go

@@ -10,9 +10,11 @@ import (
 	"path/filepath"
 	"regexp"
 	"slices"
+	"sort"
 	"strconv"
 	"strings"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 )
 
@@ -25,7 +27,16 @@ const (
 
 	// Prefix with the node dir
 	GPUTotalMemoryFileGlob = "mem_banks/*/properties" // size_in_bytes line
-	GPUUsedMemoryFileGlob  = "mem_banks/*/used_memory"
+
+	// Direct Rendering Manager sysfs location
+	DRMDeviceDirGlob   = "/sys/class/drm/card*/device"
+	DRMTotalMemoryFile = "mem_info_vram_total"
+	DRMUsedMemoryFile  = "mem_info_vram_used"
+
+	// In hex; properties file is in decimal
+	DRMUniqueIDFile = "unique_id"
+	DRMVendorFile   = "vendor"
+	DRMDeviceFile   = "device"
 )
 
 var (
@@ -35,8 +46,8 @@ var (
 )
 
 // Gather GPU information from the amdgpu driver if any supported GPUs are detected
-func AMDGetGPUInfo() []GpuInfo {
-	resp := []GpuInfo{}
+func AMDGetGPUInfo() []RocmGPUInfo {
+	resp := []RocmGPUInfo{}
 	if !AMDDetected() {
 		return resp
 	}
@@ -50,9 +61,9 @@ func AMDGetGPUInfo() []GpuInfo {
 
 	// Determine if the user has already pre-selected which GPUs to look at, then ignore the others
 	var visibleDevices []string
-	hipVD := os.Getenv("HIP_VISIBLE_DEVICES")   // zero based index only
-	rocrVD := os.Getenv("ROCR_VISIBLE_DEVICES") // zero based index or UUID, but consumer cards seem to not support UUID
-	gpuDO := os.Getenv("GPU_DEVICE_ORDINAL")    // zero based index
+	hipVD := envconfig.HipVisibleDevices()   // zero based index only
+	rocrVD := envconfig.RocrVisibleDevices() // zero based index or UUID, but consumer cards seem to not support UUID
+	gpuDO := envconfig.GpuDeviceOrdinal()    // zero based index
 	switch {
 	// TODO is this priorty order right?
 	case hipVD != "":
@@ -65,13 +76,27 @@ func AMDGetGPUInfo() []GpuInfo {
 		visibleDevices = strings.Split(gpuDO, ",")
 	}
 
-	gfxOverride := os.Getenv("HSA_OVERRIDE_GFX_VERSION")
+	gfxOverride := envconfig.HsaOverrideGfxVersion()
 	var supported []string
 	libDir := ""
 
 	// The amdgpu driver always exposes the host CPU(s) first, but we have to skip them and subtract
 	// from the other IDs to get alignment with the HIP libraries expectations (zero is the first GPU, not the CPU)
 	matches, _ := filepath.Glob(GPUPropertiesFileGlob)
+	sort.Slice(matches, func(i, j int) bool {
+		// /sys/class/kfd/kfd/topology/nodes/<number>/properties
+		a, err := strconv.ParseInt(filepath.Base(filepath.Dir(matches[i])), 10, 64)
+		if err != nil {
+			slog.Debug("parse err", "error", err, "match", matches[i])
+			return false
+		}
+		b, err := strconv.ParseInt(filepath.Base(filepath.Dir(matches[j])), 10, 64)
+		if err != nil {
+			slog.Debug("parse err", "error", err, "match", matches[i])
+			return false
+		}
+		return a < b
+	})
 	cpuCount := 0
 	for _, match := range matches {
 		slog.Debug("evaluating amdgpu node " + match)
@@ -90,7 +115,7 @@ func AMDGetGPUInfo() []GpuInfo {
 		scanner := bufio.NewScanner(fp)
 		isCPU := false
 		var major, minor, patch uint64
-		var vendor, device uint64
+		var vendor, device, uniqueID uint64
 		for scanner.Scan() {
 			line := strings.TrimSpace(scanner.Text())
 			// Note: we could also use "cpu_cores_count X" where X is greater than zero to detect CPUs
@@ -121,30 +146,43 @@ func AMDGetGPUInfo() []GpuInfo {
 			} else if strings.HasPrefix(line, "vendor_id") {
 				ver := strings.Fields(line)
 				if len(ver) != 2 {
-					slog.Debug("malformed vendor_id", "vendor_id", line)
+					slog.Debug("malformed", "vendor_id", line)
 					continue
 				}
-				vendor, err = strconv.ParseUint(ver[1], 10, 32)
+				vendor, err = strconv.ParseUint(ver[1], 10, 64)
 				if err != nil {
-					slog.Debug("malformed vendor_id" + line)
+					slog.Debug("malformed", "vendor_id", line, "error", err)
 				}
 			} else if strings.HasPrefix(line, "device_id") {
 				ver := strings.Fields(line)
 				if len(ver) != 2 {
-					slog.Debug("malformed device_id", "device_id", line)
+					slog.Debug("malformed", "device_id", line)
 					continue
 				}
-				device, err = strconv.ParseUint(ver[1], 10, 32)
+				device, err = strconv.ParseUint(ver[1], 10, 64)
 				if err != nil {
-					slog.Debug("malformed device_id" + line)
+					slog.Debug("malformed", "device_id", line, "error", err)
+				}
+			} else if strings.HasPrefix(line, "unique_id") {
+				ver := strings.Fields(line)
+				if len(ver) != 2 {
+					slog.Debug("malformed", "unique_id", line)
+					continue
+				}
+				uniqueID, err = strconv.ParseUint(ver[1], 10, 64)
+				if err != nil {
+					slog.Debug("malformed", "unique_id", line, "error", err)
 				}
 			}
-
 			// TODO - any other properties we want to extract and record?
 			// vendor_id + device_id -> pci lookup for "Name"
 			// Other metrics that may help us understand relative performance between multiple GPUs
 		}
 
+		// Note: while ./mem_banks/*/used_memory exists, it doesn't appear to take other VRAM consumers
+		// into consideration, so we instead map the device over to the DRM driver sysfs nodes which
+		// do reliably report VRAM usage.
+
 		if isCPU {
 			cpuCount++
 			continue
@@ -156,7 +194,7 @@ func AMDGetGPUInfo() []GpuInfo {
 		// Shouldn't happen, but just in case...
 		if gpuID < 0 {
 			slog.Error("unexpected amdgpu sysfs data resulted in negative GPU ID, please set OLLAMA_DEBUG=1 and report an issue")
-			return []GpuInfo{}
+			return nil
 		}
 
 		if int(major) < RocmComputeMin {
@@ -167,65 +205,68 @@ func AMDGetGPUInfo() []GpuInfo {
 		// Look up the memory for the current node
 		totalMemory := uint64(0)
 		usedMemory := uint64(0)
-		propGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(nodeID), GPUTotalMemoryFileGlob)
-		propFiles, err := filepath.Glob(propGlob)
+		var usedFile string
+		mapping := []struct {
+			id       uint64
+			filename string
+		}{
+			{vendor, DRMVendorFile},
+			{device, DRMDeviceFile},
+			{uniqueID, DRMUniqueIDFile}, // Not all devices will report this
+		}
+		slog.Debug("mapping amdgpu to drm sysfs nodes", "amdgpu", match, "vendor", vendor, "device", device, "unique_id", uniqueID)
+		// Map over to DRM location to find the total/free memory
+		drmMatches, _ := filepath.Glob(DRMDeviceDirGlob)
+		for _, devDir := range drmMatches {
+			matched := true
+			for _, m := range mapping {
+				if m.id == 0 {
+					// Null ID means it didn't populate, so we can't use it to match
+					continue
+				}
+				filename := filepath.Join(devDir, m.filename)
+				buf, err := os.ReadFile(filename)
 				if err != nil {
-			slog.Warn("error looking up total GPU memory", "glob", propGlob, "error", err)
+					slog.Debug("failed to read sysfs node", "file", filename, "error", err)
+					matched = false
+					break
 				}
-		// 1 or more memory banks - sum the values of all of them
-		for _, propFile := range propFiles {
-			fp, err := os.Open(propFile)
+				// values here are in hex, strip off the lead 0x and parse so we can compare the numeric (decimal) values in amdgpu
+				cmp, err := strconv.ParseUint(strings.TrimPrefix(strings.TrimSpace(string(buf)), "0x"), 16, 64)
 				if err != nil {
-				slog.Warn("failed to open sysfs node", "file", propFile, "erroir", err)
+					slog.Debug("failed to parse sysfs node", "file", filename, "error", err)
+					matched = false
+					break
+				}
+				if cmp != m.id {
+					matched = false
+					break
+				}
+			}
+			if !matched {
 				continue
 			}
-			defer fp.Close()
-			scanner := bufio.NewScanner(fp)
-			for scanner.Scan() {
-				line := strings.TrimSpace(scanner.Text())
-				if strings.HasPrefix(line, "size_in_bytes") {
-					ver := strings.Fields(line)
-					if len(ver) != 2 {
-						slog.Warn("malformed " + line)
-						continue
-					}
-					bankSizeInBytes, err := strconv.ParseUint(ver[1], 10, 64)
+
+			// Found the matching DRM directory
+			slog.Debug("matched", "amdgpu", match, "drm", devDir)
+			totalFile := filepath.Join(devDir, DRMTotalMemoryFile)
+			buf, err := os.ReadFile(totalFile)
 			if err != nil {
-						slog.Warn("malformed int " + line)
-						continue
+				slog.Debug("failed to read sysfs node", "file", totalFile, "error", err)
+				break
 			}
-					totalMemory += bankSizeInBytes
-				}
-			}
-		}
-		if totalMemory == 0 {
-			slog.Warn("amdgpu reports zero total memory", "gpu", gpuID)
-			continue
-		}
-		usedGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(nodeID), GPUUsedMemoryFileGlob)
-		usedFiles, err := filepath.Glob(usedGlob)
+			totalMemory, err = strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
 			if err != nil {
-			slog.Warn("error looking up used GPU memory", "glob", usedGlob, "error", err)
-			continue
+				slog.Debug("failed to parse sysfs node", "file", totalFile, "error", err)
+				break
 			}
-		for _, usedFile := range usedFiles {
-			fp, err := os.Open(usedFile)
+
+			usedFile = filepath.Join(devDir, DRMUsedMemoryFile)
+			usedMemory, err = getFreeMemory(usedFile)
 			if err != nil {
-				slog.Warn("failed to open sysfs node", "file", usedFile, "error", err)
-				continue
+				slog.Debug("failed to update used memory", "error", err)
 			}
-			defer fp.Close()
-			data, err := io.ReadAll(fp)
-			if err != nil {
-				slog.Warn("failed to read sysfs node", "file", usedFile, "error", err)
-				continue
-			}
-			used, err := strconv.ParseUint(strings.TrimSpace(string(data)), 10, 64)
-			if err != nil {
-				slog.Warn("malformed used memory", "data", string(data), "error", err)
-				continue
-			}
-			usedMemory += used
+			break
 		}
 
 		// iGPU detection, remove this check once we can support an iGPU variant of the rocm library
@@ -241,18 +282,21 @@ func AMDGetGPUInfo() []GpuInfo {
 
 		slog.Debug("amdgpu memory", "gpu", gpuID, "total", format.HumanBytes2(totalMemory))
 		slog.Debug("amdgpu memory", "gpu", gpuID, "available", format.HumanBytes2(totalMemory-usedMemory))
-		gpuInfo := GpuInfo{
+		gpuInfo := RocmGPUInfo{
+			GpuInfo: GpuInfo{
 				Library: "rocm",
 				memInfo: memInfo{
 					TotalMemory: totalMemory,
 					FreeMemory:  (totalMemory - usedMemory),
 				},
-			ID:            fmt.Sprintf("%d", gpuID),
+				ID:            strconv.Itoa(gpuID),
 				Name:          name,
 				Compute:       fmt.Sprintf("gfx%d%x%x", major, minor, patch),
 				MinimumMemory: rocmMinimumMemory,
 				DriverMajor:   driverMajor,
 				DriverMinor:   driverMinor,
+			},
+			usedFilepath: usedFile,
 		}
 
 		// If the user wants to filter to a subset of devices, filter out if we aren't a match
@@ -276,7 +320,7 @@ func AMDGetGPUInfo() []GpuInfo {
 			libDir, err = AMDValidateLibDir()
 			if err != nil {
 				slog.Warn("unable to verify rocm library, will use cpu", "error", err)
-				return []GpuInfo{}
+				return nil
 			}
 		}
 		gpuInfo.DependencyPath = libDir
@@ -287,7 +331,7 @@ func AMDGetGPUInfo() []GpuInfo {
 				supported, err = GetSupportedGFX(libDir)
 				if err != nil {
 					slog.Warn("failed to lookup supported GFX types, falling back to CPU mode", "error", err)
-					return []GpuInfo{}
+					return nil
 				}
 				slog.Debug("rocm supported GPUs", "types", supported)
 			}
@@ -304,6 +348,11 @@ func AMDGetGPUInfo() []GpuInfo {
 			slog.Info("skipping rocm gfx compatibility check", "HSA_OVERRIDE_GFX_VERSION", gfxOverride)
 		}
 
+		// Check for env var workarounds
+		if name == "1002:687f" { // Vega RX 56
+			gpuInfo.EnvWorkarounds = append(gpuInfo.EnvWorkarounds, [2]string{"HSA_ENABLE_SDMA", "0"})
+		}
+
 		// The GPU has passed all the verification steps and is supported
 		resp = append(resp, gpuInfo)
 	}
@@ -378,3 +427,31 @@ func AMDDriverVersion() (driverMajor, driverMinor int, err error) {
 	}
 	return driverMajor, driverMinor, nil
 }
+
+func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
+	if len(gpus) == 0 {
+		return nil
+	}
+	for i := range gpus {
+		usedMemory, err := getFreeMemory(gpus[i].usedFilepath)
+		if err != nil {
+			return err
+		}
+		slog.Debug("updating rocm free memory", "gpu", gpus[i].ID, "name", gpus[i].Name, "before", format.HumanBytes2(gpus[i].FreeMemory), "now", format.HumanBytes2(gpus[i].TotalMemory-usedMemory))
+		gpus[i].FreeMemory = gpus[i].TotalMemory - usedMemory
+	}
+	return nil
+}
+
+func getFreeMemory(usedFile string) (uint64, error) {
+	buf, err := os.ReadFile(usedFile)
+	if err != nil {
+		return 0, fmt.Errorf("failed to read sysfs node %s %w", usedFile, err)
+	}
+	usedMemory, err := strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
+	if err != nil {
+		slog.Debug("failed to parse sysfs node", "file", usedFile, "error", err)
+		return 0, fmt.Errorf("failed to parse sysfs node %s %w", usedFile, err)
+	}
+	return usedMemory, nil
+}

gpu/amd_windows.go

@@ -7,8 +7,10 @@ import (
 	"os"
 	"path/filepath"
 	"slices"
+	"strconv"
 	"strings"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 )
 
@@ -20,12 +22,12 @@ const (
 
 var (
 	// Used to validate if the given ROCm lib is usable
-	ROCmLibGlobs          = []string{"hipblas.dll", "rocblas"}                 // TODO - probably include more coverage of files here...
-	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\5.7\\bin"} // TODO glob?
+	ROCmLibGlobs          = []string{"hipblas.dll", "rocblas"}                 // This is not sufficient to discern v5 vs v6
+	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\6.1\\bin"} // TODO glob?
 )
 
-func AMDGetGPUInfo() []GpuInfo {
-	resp := []GpuInfo{}
+func AMDGetGPUInfo() []RocmGPUInfo {
+	resp := []RocmGPUInfo{}
 	hl, err := NewHipLib()
 	if err != nil {
 		slog.Debug(err.Error())
@@ -33,12 +35,11 @@ func AMDGetGPUInfo() []GpuInfo {
 	}
 	defer hl.Release()
 
-	// TODO - this reports incorrect version information, so omitting for now
-	// driverMajor, driverMinor, err := hl.AMDDriverVersion()
-	// if err != nil {
-	// 	// For now this is benign, but we may eventually need to fail compatibility checks
-	// 	slog.Debug("error looking up amd driver version", "error", err)
-	// }
+	driverMajor, driverMinor, err := hl.AMDDriverVersion()
+	if err != nil {
+		// For now this is benign, but we may eventually need to fail compatibility checks
+		slog.Debug("error looking up amd driver version", "error", err)
+	}
 
 	// Note: the HIP library automatically handles subsetting to any HIP_VISIBLE_DEVICES the user specified
 	count := hl.HipGetDeviceCount()
@@ -52,7 +53,7 @@ func AMDGetGPUInfo() []GpuInfo {
 	}
 
 	var supported []string
-	gfxOverride := os.Getenv("HSA_OVERRIDE_GFX_VERSION")
+	gfxOverride := envconfig.HsaOverrideGfxVersion()
 	if gfxOverride == "" {
 		supported, err = GetSupportedGFX(libDir)
 		if err != nil {
@@ -65,7 +66,7 @@ func AMDGetGPUInfo() []GpuInfo {
 
 	slog.Debug("detected hip devices", "count", count)
 	// TODO how to determine the underlying device ID when visible devices is causing this to subset?
-	for i := 0; i < count; i++ {
+	for i := range count {
 		err = hl.HipSetDevice(i)
 		if err != nil {
 			slog.Warn("set device", "id", i, "error", err)
@@ -91,7 +92,8 @@ func AMDGetGPUInfo() []GpuInfo {
 			continue
 		}
 		if gfxOverride == "" {
-			if !slices.Contains[[]string, string](supported, gfx) {
+			// Strip off Target Features when comparing
+			if !slices.Contains[[]string, string](supported, strings.Split(gfx, ":")[0]) {
 				slog.Warn("amdgpu is not supported", "gpu", i, "gpu_type", gfx, "library", libDir, "supported_types", supported)
 				// TODO - consider discrete markdown just for ROCM troubleshooting?
 				slog.Warn("See https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md for HSA_OVERRIDE_GFX_VERSION usage")
@@ -113,25 +115,27 @@ func AMDGetGPUInfo() []GpuInfo {
 			continue
 		}
 
-		// TODO revisit this once ROCm v6 is available on windows.
-		// v5.7 only reports VRAM used by this process, so it's completely wrong and unusable
 		slog.Debug("amdgpu memory", "gpu", i, "total", format.HumanBytes2(totalMemory))
 		slog.Debug("amdgpu memory", "gpu", i, "available", format.HumanBytes2(freeMemory))
-		gpuInfo := GpuInfo{
+		gpuInfo := RocmGPUInfo{
+			GpuInfo: GpuInfo{
 				Library: "rocm",
 				memInfo: memInfo{
 					TotalMemory: totalMemory,
 					FreeMemory:  freeMemory,
 				},
-			ID:             fmt.Sprintf("%d", i), // TODO this is probably wrong if we specify visible devices
+				// Free memory reporting on Windows is not reliable until we bump to ROCm v6.2
+				UnreliableFreeMemory: true,
+
+				ID:             strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
 				DependencyPath: libDir,
 				MinimumMemory:  rocmMinimumMemory,
 				Name:           name,
 				Compute:        gfx,
-
-			// TODO - this information isn't accurate on windows, so don't report it until we find the right way to retrieve
-			// DriverMajor:    driverMajor,
-			// DriverMinor:    driverMinor,
+				DriverMajor:    driverMajor,
+				DriverMinor:    driverMinor,
+			},
+			index: i,
 		}
 
 		resp = append(resp, gpuInfo)
@@ -159,3 +163,30 @@ func AMDValidateLibDir() (string, error) {
 	slog.Warn("amdgpu detected, but no compatible rocm library found.  Please install ROCm")
 	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }
+
+func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
+	if len(gpus) == 0 {
+		return nil
+	}
+	hl, err := NewHipLib()
+	if err != nil {
+		slog.Debug(err.Error())
+		return nil
+	}
+	defer hl.Release()
+
+	for i := range gpus {
+		err := hl.HipSetDevice(gpus[i].index)
+		if err != nil {
+			return err
+		}
+		freeMemory, _, err := hl.HipMemGetInfo()
+		if err != nil {
+			slog.Warn("get mem info", "id", i, "error", err)
+			continue
+		}
+		slog.Debug("updating rocm free memory", "gpu", gpus[i].ID, "name", gpus[i].Name, "before", format.HumanBytes2(gpus[i].FreeMemory), "now", format.HumanBytes2(freeMemory))
+		gpus[i].FreeMemory = freeMemory
+	}
+	return nil
+}

gpu/assets.go

@@ -13,7 +13,7 @@ import (
 	"syscall"
 	"time"
 
-	"github.com/ollama/ollama/server/envconfig"
+	"github.com/ollama/ollama/envconfig"
 )
 
 var (
@@ -26,7 +26,7 @@ func PayloadsDir() (string, error) {
 	defer lock.Unlock()
 	var err error
 	if payloadsDir == "" {
-		runnersDir := envconfig.RunnersDir
+		runnersDir := envconfig.RunnersDir()
 
 		if runnersDir != "" {
 			payloadsDir = runnersDir
@@ -35,7 +35,7 @@ func PayloadsDir() (string, error) {
 
 		// The remainder only applies on non-windows where we still carry payloads in the main executable
 		cleanupTmpDirs()
-		tmpDir := envconfig.TmpDir
+		tmpDir := envconfig.TmpDir()
 		if tmpDir == "" {
 			tmpDir, err = os.MkdirTemp("", "ollama")
 			if err != nil {
@@ -77,20 +77,27 @@ func cleanupTmpDirs() {
 			continue
 		}
 		raw, err := os.ReadFile(filepath.Join(d, "ollama.pid"))
-		if err == nil {
+		if err != nil {
+			slog.Warn("failed to read ollama.pid", "path", d, "error", err)
+			// No pid, ignore this tmpdir
+			continue
+		}
+
 		pid, err := strconv.Atoi(string(raw))
-			if err == nil {
-				if proc, err := os.FindProcess(int(pid)); err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
+		if err != nil {
+			slog.Warn("failed to parse pid", "path", d, "error", err)
+			continue
+		}
+
+		proc, err := os.FindProcess(pid)
+		if err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
+			slog.Warn("found running ollama", "pid", pid, "path", d)
 			// Another running ollama, ignore this tmpdir
 			continue
 		}
-			}
-		} else {
-			slog.Debug("failed to open ollama.pid", "path", d, "error", err)
-		}
-		err = os.RemoveAll(d)
-		if err != nil {
-			slog.Debug("unable to cleanup stale tmpdir", "path", d, "error", err)
+
+		if err := os.Remove(d); err != nil {
+			slog.Warn("unable to cleanup stale tmpdir", "path", d, "error", err)
 		}
 	}
 }
@@ -98,7 +105,7 @@ func cleanupTmpDirs() {
 func Cleanup() {
 	lock.Lock()
 	defer lock.Unlock()
-	runnersDir := envconfig.RunnersDir
+	runnersDir := envconfig.RunnersDir()
 	if payloadsDir != "" && runnersDir == "" && runtime.GOOS != "windows" {
 		// We want to fully clean up the tmpdir parent of the payloads dir
 		tmpDir := filepath.Clean(filepath.Join(payloadsDir, ".."))

gpu/cpu_common.go

@@ -1,21 +1,16 @@
 package gpu
 
 import (
-	"log/slog"
-
 	"golang.org/x/sys/cpu"
 )
 
-func GetCPUVariant() string {
+func GetCPUCapability() CPUCapability {
 	if cpu.X86.HasAVX2 {
-		slog.Debug("CPU has AVX2")
-		return "avx2"
+		return CPUCapabilityAVX2
 	}
 	if cpu.X86.HasAVX {
-		slog.Debug("CPU has AVX")
-		return "avx"
+		return CPUCapabilityAVX
 	}
-	slog.Debug("CPU does not have vector extensions")
 	// else LCD
-	return ""
+	return CPUCapabilityNone
 }

gpu/cuda_common.go

@@ -18,5 +18,4 @@ func cudaGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
 		ids = append(ids, info.ID)
 	}
 	return "CUDA_VISIBLE_DEVICES", strings.Join(ids, ",")
-
 }

gpu/gpu.go

@@ -20,22 +20,41 @@ import (
 	"sync"
 	"unsafe"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/server/envconfig"
 )
 
-type handles struct {
+type cudaHandles struct {
 	deviceCount int
 	cudart      *C.cudart_handle_t
 	nvcuda      *C.nvcuda_handle_t
+	nvml        *C.nvml_handle_t
+}
+
+type oneapiHandles struct {
+	oneapi      *C.oneapi_handle_t
+	deviceCount int
 }
 
 const (
 	cudaMinimumMemory = 457 * format.MebiByte
 	rocmMinimumMemory = 457 * format.MebiByte
+	// TODO OneAPI minimum memory
 )
 
-var gpuMutex sync.Mutex
+var (
+	gpuMutex      sync.Mutex
+	bootstrapped  bool
+	cpuCapability CPUCapability
+	cpus          []CPUInfo
+	cudaGPUs      []CudaGPUInfo
+	nvcudaLibPath string
+	cudartLibPath string
+	oneapiLibPath string
+	nvmlLibPath   string
+	rocmGPUs      []RocmGPUInfo
+	oneapiGPUs    []OneapiGPUInfo
+)
 
 // With our current CUDA compile flags, older than 5.0 will not work properly
 var CudaComputeMin = [2]C.int{5, 0}
@@ -45,103 +64,113 @@ var RocmComputeMin = 9
 // TODO find a better way to detect iGPU instead of minimum memory
 const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU
 
-var CudartLinuxGlobs = []string{
-	"/usr/local/cuda/lib64/libcudart.so*",
-	"/usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so*",
-	"/usr/lib/x86_64-linux-gnu/libcudart.so*",
-	"/usr/lib/wsl/lib/libcudart.so*",
-	"/usr/lib/wsl/drivers/*/libcudart.so*",
-	"/opt/cuda/lib64/libcudart.so*",
-	"/usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so*",
-	"/usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so*",
-	"/usr/lib/aarch64-linux-gnu/libcudart.so*",
-	"/usr/local/cuda/lib*/libcudart.so*",
-	"/usr/lib*/libcudart.so*",
-	"/usr/local/lib*/libcudart.so*",
-}
-
-var CudartWindowsGlobs = []string{
-	"c:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v*\\bin\\cudart64_*.dll",
-}
-
-var NvcudaLinuxGlobs = []string{
-	"/usr/local/cuda*/targets/*/lib/libcuda.so*",
-	"/usr/lib/*-linux-gnu/nvidia/current/libcuda.so*",
-	"/usr/lib/*-linux-gnu/libcuda.so*",
-	"/usr/lib/wsl/lib/libcuda.so*",
-	"/usr/lib/wsl/drivers/*/libcuda.so*",
-	"/opt/cuda/lib*/libcuda.so*",
-	"/usr/local/cuda/lib*/libcuda.so*",
-	"/usr/lib*/libcuda.so*",
-	"/usr/local/lib*/libcuda.so*",
-}
-
-var NvcudaWindowsGlobs = []string{
-	"c:\\windows\\system*\\nvcuda.dll",
-}
-
 // Jetson devices have JETSON_JETPACK="x.y.z" factory set to the Jetpack version installed.
 // Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
 var CudaTegra string = os.Getenv("JETSON_JETPACK")
 
 // Note: gpuMutex must already be held
-func initGPUHandles() *handles {
+func initCudaHandles() *cudaHandles {
 
 	// TODO - if the ollama build is CPU only, don't do these checks as they're irrelevant and confusing
 
-	gpuHandles := &handles{}
-	var cudartMgmtName string
-	var cudartMgmtPatterns []string
-	var nvcudaMgmtName string
-	var nvcudaMgmtPatterns []string
+	cHandles := &cudaHandles{}
+	// Short Circuit if we already know which library to use
+	if nvmlLibPath != "" {
+		cHandles.nvml, _ = LoadNVMLMgmt([]string{nvmlLibPath})
+		return cHandles
+	}
+	if nvcudaLibPath != "" {
+		cHandles.deviceCount, cHandles.nvcuda, _ = LoadNVCUDAMgmt([]string{nvcudaLibPath})
+		return cHandles
+	}
+	if cudartLibPath != "" {
+		cHandles.deviceCount, cHandles.cudart, _ = LoadCUDARTMgmt([]string{cudartLibPath})
+		return cHandles
+	}
+
+	slog.Debug("searching for GPU discovery libraries for NVIDIA")
+	var cudartMgmtPatterns []string
 
-	tmpDir, _ := PayloadsDir()
-	switch runtime.GOOS {
-	case "windows":
-		cudartMgmtName = "cudart64_*.dll"
-		localAppData := os.Getenv("LOCALAPPDATA")
-		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", cudartMgmtName)}
-		cudartMgmtPatterns = append(cudartMgmtPatterns, CudartWindowsGlobs...)
 	// Aligned with driver, we can't carry as payloads
-		nvcudaMgmtName = "nvcuda.dll"
-		nvcudaMgmtPatterns = NvcudaWindowsGlobs
-	case "linux":
-		cudartMgmtName = "libcudart.so*"
+	nvcudaMgmtPatterns := NvcudaGlobs
+
+	if runtime.GOOS == "windows" {
+		localAppData := os.Getenv("LOCALAPPDATA")
+		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
+	}
+	tmpDir, _ := PayloadsDir()
 	if tmpDir != "" {
 		// TODO - add "payloads" for subprocess
-			cudartMgmtPatterns = []string{filepath.Join(tmpDir, "cuda*", cudartMgmtName)}
+		cudartMgmtPatterns = []string{filepath.Join(tmpDir, "cuda*", CudartMgmtName)}
+	}
+	cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
+
+	if len(NvmlGlobs) > 0 {
+		nvmlLibPaths := FindGPULibs(NvmlMgmtName, NvmlGlobs)
+		if len(nvmlLibPaths) > 0 {
+			nvml, libPath := LoadNVMLMgmt(nvmlLibPaths)
+			if nvml != nil {
+				slog.Debug("nvidia-ml loaded", "library", libPath)
+				cHandles.nvml = nvml
+				nvmlLibPath = libPath
+			}
 		}
-		cudartMgmtPatterns = append(cudartMgmtPatterns, CudartLinuxGlobs...)
-		// Aligned with driver, we can't carry as payloads
-		nvcudaMgmtName = "libcuda.so*"
-		nvcudaMgmtPatterns = NvcudaLinuxGlobs
-	default:
-		return gpuHandles
 	}
 
-	slog.Debug("Detecting GPUs")
-	nvcudaLibPaths := FindGPULibs(nvcudaMgmtName, nvcudaMgmtPatterns)
+	nvcudaLibPaths := FindGPULibs(NvcudaMgmtName, nvcudaMgmtPatterns)
 	if len(nvcudaLibPaths) > 0 {
 		deviceCount, nvcuda, libPath := LoadNVCUDAMgmt(nvcudaLibPaths)
 		if nvcuda != nil {
 			slog.Debug("detected GPUs", "count", deviceCount, "library", libPath)
-			gpuHandles.nvcuda = nvcuda
-			gpuHandles.deviceCount = deviceCount
-			return gpuHandles
+			cHandles.nvcuda = nvcuda
+			cHandles.deviceCount = deviceCount
+			nvcudaLibPath = libPath
+			return cHandles
 		}
 	}
 
-	cudartLibPaths := FindGPULibs(cudartMgmtName, cudartMgmtPatterns)
+	cudartLibPaths := FindGPULibs(CudartMgmtName, cudartMgmtPatterns)
 	if len(cudartLibPaths) > 0 {
 		deviceCount, cudart, libPath := LoadCUDARTMgmt(cudartLibPaths)
 		if cudart != nil {
 			slog.Debug("detected GPUs", "library", libPath, "count", deviceCount)
-			gpuHandles.cudart = cudart
-			gpuHandles.deviceCount = deviceCount
-			return gpuHandles
+			cHandles.cudart = cudart
+			cHandles.deviceCount = deviceCount
+			cudartLibPath = libPath
+			return cHandles
 		}
 	}
-	return gpuHandles
+
+	return cHandles
+}
+
+// Note: gpuMutex must already be held
+func initOneAPIHandles() *oneapiHandles {
+	oHandles := &oneapiHandles{}
+
+	// Short Circuit if we already know which library to use
+	if oneapiLibPath != "" {
+		oHandles.deviceCount, oHandles.oneapi, _ = LoadOneapiMgmt([]string{oneapiLibPath})
+		return oHandles
+	}
+
+	oneapiLibPaths := FindGPULibs(OneapiMgmtName, OneapiGlobs)
+	if len(oneapiLibPaths) > 0 {
+		oHandles.deviceCount, oHandles.oneapi, oneapiLibPath = LoadOneapiMgmt(oneapiLibPaths)
+	}
+
+	return oHandles
+}
+
+func GetCPUInfo() GpuInfoList {
+	gpuMutex.Lock()
+	if !bootstrapped {
+		gpuMutex.Unlock()
+		GetGPUInfo()
+	} else {
+		gpuMutex.Unlock()
+	}
+	return GpuInfoList{cpus[0].GpuInfo}
 }
 
 func GetGPUInfo() GpuInfoList {
@@ -149,49 +178,82 @@ func GetGPUInfo() GpuInfoList {
 	// GPUs so we can report warnings if we see Nvidia/AMD but fail to load the libraries
 	gpuMutex.Lock()
 	defer gpuMutex.Unlock()
-
-	gpuHandles := initGPUHandles()
+	needRefresh := true
+	var cHandles *cudaHandles
+	var oHandles *oneapiHandles
 	defer func() {
-		if gpuHandles.cudart != nil {
-			C.cudart_release(*gpuHandles.cudart)
+		if cHandles != nil {
+			if cHandles.cudart != nil {
+				C.cudart_release(*cHandles.cudart)
+			}
+			if cHandles.nvcuda != nil {
+				C.nvcuda_release(*cHandles.nvcuda)
+			}
+			if cHandles.nvml != nil {
+				C.nvml_release(*cHandles.nvml)
+			}
+		}
+		if oHandles != nil {
+			if oHandles.oneapi != nil {
+				// TODO - is this needed?
+				C.oneapi_release(*oHandles.oneapi)
 			}
-		if gpuHandles.nvcuda != nil {
-			C.nvcuda_release(*gpuHandles.nvcuda)
 		}
 	}()
 
-	// All our GPU builds on x86 have AVX enabled, so fallback to CPU if we don't detect at least AVX
-	cpuVariant := GetCPUVariant()
-	if cpuVariant == "" && runtime.GOARCH == "amd64" {
-		slog.Warn("CPU does not have AVX or AVX2, disabling GPU support.")
+	if !bootstrapped {
+		slog.Info("looking for compatible GPUs")
+		needRefresh = false
+		cpuCapability = GetCPUCapability()
+		var memInfo C.mem_info_t
+
+		mem, err := GetCPUMem()
+		if err != nil {
+			slog.Warn("error looking up system memory", "error", err)
+		}
+		cpus = []CPUInfo{CPUInfo{
+			GpuInfo: GpuInfo{
+				memInfo: mem,
+				Library: "cpu",
+				Variant: cpuCapability,
+				ID:      "0",
+			},
+		}}
+
+		// Fallback to CPU mode if we're lacking required vector extensions on x86
+		if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
+			slog.Warn("CPU does not have minimum vector extensions, GPU inference disabled", "required", GPURunnerCPUCapability, "detected", cpuCapability)
+			bootstrapped = true
+			// No need to do any GPU discovery, since we can't run on them
+			return GpuInfoList{cpus[0].GpuInfo}
 		}
 
 		// On windows we bundle the nvidia library one level above the runner dir
 		depPath := ""
-	if runtime.GOOS == "windows" && envconfig.RunnersDir != "" {
-		depPath = filepath.Dir(envconfig.RunnersDir)
+		if runtime.GOOS == "windows" && envconfig.RunnersDir() != "" {
+			depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir()), "cuda")
 		}
 
-	var memInfo C.mem_info_t
-	resp := []GpuInfo{}
+		// Load ALL libraries
+		cHandles = initCudaHandles()
 
-	// NVIDIA first
-	for i := 0; i < gpuHandles.deviceCount; i++ {
-		// TODO once we support CPU compilation variants of GPU libraries refine this...
-		if cpuVariant == "" && runtime.GOARCH == "amd64" {
-			continue
-		}
-		gpuInfo := GpuInfo{
+		// NVIDIA
+		for i := range cHandles.deviceCount {
+			if cHandles.cudart != nil || cHandles.nvcuda != nil {
+				gpuInfo := CudaGPUInfo{
+					GpuInfo: GpuInfo{
 						Library: "cuda",
+					},
+					index: i,
 				}
 				var driverMajor int
 				var driverMinor int
-		if gpuHandles.cudart != nil {
-			C.cudart_check_vram(*gpuHandles.cudart, C.int(i), &memInfo)
+				if cHandles.cudart != nil {
+					C.cudart_bootstrap(*cHandles.cudart, C.int(i), &memInfo)
 				} else {
-			C.nvcuda_check_vram(*gpuHandles.nvcuda, C.int(i), &memInfo)
-			driverMajor = int(gpuHandles.nvcuda.driver_major)
-			driverMinor = int(gpuHandles.nvcuda.driver_minor)
+					C.nvcuda_bootstrap(*cHandles.nvcuda, C.int(i), &memInfo)
+					driverMajor = int(cHandles.nvcuda.driver_major)
+					driverMinor = int(cHandles.nvcuda.driver_minor)
 				}
 				if memInfo.err != nil {
 					slog.Info("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
@@ -209,50 +271,195 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.MinimumMemory = cudaMinimumMemory
 				gpuInfo.DependencyPath = depPath
 				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-		gpuInfo.DriverMajor = int(driverMajor)
-		gpuInfo.DriverMinor = int(driverMinor)
+				gpuInfo.DriverMajor = driverMajor
+				gpuInfo.DriverMinor = driverMinor
+
+				// query the management library as well so we can record any skew between the two
+				// which represents overhead on the GPU we must set aside on subsequent updates
+				if cHandles.nvml != nil {
+					C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)
+					if memInfo.err != nil {
+						slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
+						C.free(unsafe.Pointer(memInfo.err))
+					} else {
+						if memInfo.free != 0 && uint64(memInfo.free) > gpuInfo.FreeMemory {
+							gpuInfo.OSOverhead = uint64(memInfo.free) - gpuInfo.FreeMemory
+							slog.Info("detected OS VRAM overhead",
+								"id", gpuInfo.ID,
+								"library", gpuInfo.Library,
+								"compute", gpuInfo.Compute,
+								"driver", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor),
+								"name", gpuInfo.Name,
+								"overhead", format.HumanBytes2(gpuInfo.OSOverhead),
+							)
+						}
+					}
+				}
 
 				// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
-		resp = append(resp, gpuInfo)
+				cudaGPUs = append(cudaGPUs, gpuInfo)
+			}
 		}
 
-	// Then AMD
-	resp = append(resp, AMDGetGPUInfo()...)
+		// Intel
+		if envconfig.IntelGPU() {
+			oHandles = initOneAPIHandles()
+			// On windows we bundle the oneapi library one level above the runner dir
+			depPath = ""
+			if runtime.GOOS == "windows" && envconfig.RunnersDir() != "" {
+				depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir()), "oneapi")
+			}
 
-	if len(resp) == 0 {
-		C.cpu_check_ram(&memInfo)
-		if memInfo.err != nil {
-			slog.Info("error looking up CPU memory", "error", C.GoString(memInfo.err))
-			C.free(unsafe.Pointer(memInfo.err))
-			return resp
+			for d := range oHandles.oneapi.num_drivers {
+				if oHandles.oneapi == nil {
+					// shouldn't happen
+					slog.Warn("nil oneapi handle with driver count", "count", int(oHandles.oneapi.num_drivers))
+					continue
 				}
-		gpuInfo := GpuInfo{
-			Library: "cpu",
-			Variant: cpuVariant,
+				devCount := C.oneapi_get_device_count(*oHandles.oneapi, C.int(d))
+				for i := range devCount {
+					gpuInfo := OneapiGPUInfo{
+						GpuInfo: GpuInfo{
+							Library: "oneapi",
+						},
+						driverIndex: int(d),
+						gpuIndex:    int(i),
 					}
+					// TODO - split bootstrapping from updating free memory
+					C.oneapi_check_vram(*oHandles.oneapi, C.int(d), i, &memInfo)
+					// TODO - convert this to MinimumMemory based on testing...
+					var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
+					memInfo.free = C.uint64_t(totalFreeMem)
 					gpuInfo.TotalMemory = uint64(memInfo.total)
 					gpuInfo.FreeMemory = uint64(memInfo.free)
 					gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
-
-		resp = append(resp, gpuInfo)
+					gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
+					gpuInfo.DependencyPath = depPath
+					oneapiGPUs = append(oneapiGPUs, gpuInfo)
+				}
+			}
 		}
 
+		rocmGPUs = AMDGetGPUInfo()
+		bootstrapped = true
+		if len(cudaGPUs) == 0 && len(rocmGPUs) == 0 && len(oneapiGPUs) == 0 {
+			slog.Info("no compatible GPUs were discovered")
+		}
+	}
+
+	// For detected GPUs, load library if not loaded
+
+	// Refresh free memory usage
+	if needRefresh {
+		mem, err := GetCPUMem()
+		if err != nil {
+			slog.Warn("error looking up system memory", "error", err)
+		} else {
+			slog.Debug("updating system memory data",
+				slog.Group(
+					"before",
+					"total", format.HumanBytes2(cpus[0].TotalMemory),
+					"free", format.HumanBytes2(cpus[0].FreeMemory),
+					"free_swap", format.HumanBytes2(cpus[0].FreeSwap),
+				),
+				slog.Group(
+					"now",
+					"total", format.HumanBytes2(mem.TotalMemory),
+					"free", format.HumanBytes2(mem.FreeMemory),
+					"free_swap", format.HumanBytes2(mem.FreeSwap),
+				),
+			)
+			cpus[0].FreeMemory = mem.FreeMemory
+			cpus[0].FreeSwap = mem.FreeSwap
+		}
+
+		var memInfo C.mem_info_t
+		if cHandles == nil && len(cudaGPUs) > 0 {
+			cHandles = initCudaHandles()
+		}
+		for i, gpu := range cudaGPUs {
+			if cHandles.nvml != nil {
+				C.nvml_get_free(*cHandles.nvml, C.int(gpu.index), &memInfo.free, &memInfo.total, &memInfo.used)
+			} else if cHandles.cudart != nil {
+				C.cudart_bootstrap(*cHandles.cudart, C.int(gpu.index), &memInfo)
+			} else if cHandles.nvcuda != nil {
+				C.nvcuda_get_free(*cHandles.nvcuda, C.int(gpu.index), &memInfo.free, &memInfo.total)
+				memInfo.used = memInfo.total - memInfo.free
+			} else {
+				// shouldn't happen
+				slog.Warn("no valid cuda library loaded to refresh vram usage")
+				break
+			}
+			if memInfo.err != nil {
+				slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
+				C.free(unsafe.Pointer(memInfo.err))
+				continue
+			}
+			if memInfo.free == 0 {
+				slog.Warn("error looking up nvidia GPU memory")
+				continue
+			}
+			if cHandles.nvml != nil && gpu.OSOverhead > 0 {
+				// When using the management library update based on recorded overhead
+				memInfo.free -= C.uint64_t(gpu.OSOverhead)
+			}
+			slog.Debug("updating cuda memory data",
+				"gpu", gpu.ID,
+				"name", gpu.Name,
+				"overhead", format.HumanBytes2(gpu.OSOverhead),
+				slog.Group(
+					"before",
+					"total", format.HumanBytes2(gpu.TotalMemory),
+					"free", format.HumanBytes2(gpu.FreeMemory),
+				),
+				slog.Group(
+					"now",
+					"total", format.HumanBytes2(uint64(memInfo.total)),
+					"free", format.HumanBytes2(uint64(memInfo.free)),
+					"used", format.HumanBytes2(uint64(memInfo.used)),
+				),
+			)
+			cudaGPUs[i].FreeMemory = uint64(memInfo.free)
+		}
+
+		if oHandles == nil && len(oneapiGPUs) > 0 {
+			oHandles = initOneAPIHandles()
+		}
+		for i, gpu := range oneapiGPUs {
+			if oHandles.oneapi == nil {
+				// shouldn't happen
+				slog.Warn("nil oneapi handle with device count", "count", oHandles.deviceCount)
+				continue
+			}
+			C.oneapi_check_vram(*oHandles.oneapi, C.int(gpu.driverIndex), C.int(gpu.gpuIndex), &memInfo)
+			// TODO - convert this to MinimumMemory based on testing...
+			var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
+			memInfo.free = C.uint64_t(totalFreeMem)
+			oneapiGPUs[i].FreeMemory = uint64(memInfo.free)
+		}
+
+		err = RocmGPUInfoList(rocmGPUs).RefreshFreeMemory()
+		if err != nil {
+			slog.Debug("problem refreshing ROCm free memory", "error", err)
+		}
+	}
+
+	resp := []GpuInfo{}
+	for _, gpu := range cudaGPUs {
+		resp = append(resp, gpu.GpuInfo)
+	}
+	for _, gpu := range rocmGPUs {
+		resp = append(resp, gpu.GpuInfo)
+	}
+	for _, gpu := range oneapiGPUs {
+		resp = append(resp, gpu.GpuInfo)
+	}
+	if len(resp) == 0 {
+		resp = append(resp, cpus[0].GpuInfo)
+	}
 	return resp
 }
 
-func GetCPUMem() (memInfo, error) {
-	var ret memInfo
-	var info C.mem_info_t
-	C.cpu_check_ram(&info)
-	if info.err != nil {
-		defer C.free(unsafe.Pointer(info.err))
-		return ret, fmt.Errorf(C.GoString(info.err))
-	}
-	ret.FreeMemory = uint64(info.free)
-	ret.TotalMemory = uint64(info.total)
-	return ret, nil
-}
-
 func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
 	var ldPaths []string
@@ -283,6 +490,7 @@ func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 		// Nvidia PhysX known to return bogus results
 		if strings.Contains(pattern, "PhysX") {
 			slog.Debug("skipping PhysX cuda library path", "path", pattern)
+			continue
 		}
 		// Ignore glob discovery errors
 		matches, _ := filepath.Glob(pattern)
@@ -339,7 +547,23 @@ func LoadNVCUDAMgmt(nvcudaLibPaths []string) (int, *C.nvcuda_handle_t, string) {
 		defer C.free(unsafe.Pointer(lib))
 		C.nvcuda_init(lib, &resp)
 		if resp.err != nil {
-			slog.Debug("Unable to load nvcuda", "library", libPath, "error", C.GoString(resp.err))
+			// Decide what log level based on the type of error message to help users understand why
+			msg := C.GoString(resp.err)
+			switch resp.cudaErr {
+			case C.CUDA_ERROR_INSUFFICIENT_DRIVER, C.CUDA_ERROR_SYSTEM_DRIVER_MISMATCH:
+				slog.Warn("version mismatch between driver and cuda driver library - reboot or upgrade may be required", "library", libPath, "error", msg)
+			case C.CUDA_ERROR_NO_DEVICE:
+				slog.Info("no nvidia devices detected", "library", libPath)
+			case C.CUDA_ERROR_UNKNOWN:
+				slog.Warn("unknown error initializing cuda driver library", "library", libPath, "error", msg)
+				slog.Warn("see https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md for more information")
+			default:
+				if strings.Contains(msg, "wrong ELF class") {
+					slog.Debug("skipping 32bit library", "library", libPath)
+				} else {
+					slog.Info("unable to load cuda driver library", "library", libPath, "error", msg)
+				}
+			}
 			C.free(unsafe.Pointer(resp.err))
 		} else {
 			return int(resp.num_devices), &resp.ch, libPath
@@ -348,8 +572,46 @@ func LoadNVCUDAMgmt(nvcudaLibPaths []string) (int, *C.nvcuda_handle_t, string) {
 	return 0, nil, ""
 }
 
+func LoadNVMLMgmt(nvmlLibPaths []string) (*C.nvml_handle_t, string) {
+	var resp C.nvml_init_resp_t
+	resp.ch.verbose = getVerboseState()
+	for _, libPath := range nvmlLibPaths {
+		lib := C.CString(libPath)
+		defer C.free(unsafe.Pointer(lib))
+		C.nvml_init(lib, &resp)
+		if resp.err != nil {
+			slog.Info(fmt.Sprintf("Unable to load NVML management library %s: %s", libPath, C.GoString(resp.err)))
+			C.free(unsafe.Pointer(resp.err))
+		} else {
+			return &resp.ch, libPath
+		}
+	}
+	return nil, ""
+}
+
+func LoadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string) {
+	var resp C.oneapi_init_resp_t
+	num_devices := 0
+	resp.oh.verbose = getVerboseState()
+	for _, libPath := range oneapiLibPaths {
+		lib := C.CString(libPath)
+		defer C.free(unsafe.Pointer(lib))
+		C.oneapi_init(lib, &resp)
+		if resp.err != nil {
+			slog.Debug("Unable to load oneAPI management library", "library", libPath, "error", C.GoString(resp.err))
+			C.free(unsafe.Pointer(resp.err))
+		} else {
+			for i := range resp.oh.num_drivers {
+				num_devices += int(C.oneapi_get_device_count(resp.oh, C.int(i)))
+			}
+			return num_devices, &resp.oh, libPath
+		}
+	}
+	return 0, nil, ""
+}
+
 func getVerboseState() C.uint16_t {
-	if envconfig.Debug {
+	if envconfig.Debug() {
 		return C.uint16_t(1)
 	}
 	return C.uint16_t(0)
@@ -368,6 +630,8 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 		return cudaGetVisibleDevicesEnv(l)
 	case "rocm":
 		return rocmGetVisibleDevicesEnv(l)
+	case "oneapi":
+		return oneapiGetVisibleDevicesEnv(l)
 	default:
 		slog.Debug("no filter required for library " + l[0].Library)
 		return "", ""

gpu/gpu_darwin.go

@@ -24,7 +24,7 @@ func GetGPUInfo() GpuInfoList {
 		return []GpuInfo{
 			{
 				Library: "cpu",
-				Variant: GetCPUVariant(),
+				Variant: GetCPUCapability(),
 				memInfo: mem,
 			},
 		}
@@ -42,10 +42,22 @@ func GetGPUInfo() GpuInfoList {
 	return []GpuInfo{info}
 }
 
+func GetCPUInfo() GpuInfoList {
+	mem, _ := GetCPUMem()
+	return []GpuInfo{
+		{
+			Library: "cpu",
+			Variant: GetCPUCapability(),
+			memInfo: mem,
+		},
+	}
+}
+
 func GetCPUMem() (memInfo, error) {
 	return memInfo{
 		TotalMemory: uint64(C.getPhysicalMemory()),
-		FreeMemory:  0,
+		FreeMemory:  uint64(C.getFreeMemory()),
+		// FreeSwap omitted as Darwin uses dynamic paging
 	}, nil
 }
 

gpu/gpu_info.h

@@ -47,6 +47,7 @@ typedef struct mem_info {
   char gpu_name[GPU_NAME_LEN];
   uint64_t total;
   uint64_t free;
+  uint64_t used;
 
   // Compute Capability
   int major; 
@@ -62,6 +63,8 @@ void cpu_check_ram(mem_info_t *resp);
 
 #include "gpu_info_cudart.h"
 #include "gpu_info_nvcuda.h"
+#include "gpu_info_nvml.h"
+#include "gpu_info_oneapi.h"
 
 #endif  // __GPU_INFO_H__
 #endif  // __APPLE__

gpu/gpu_info_cpu.c

@@ -1,45 +0,0 @@
-#include "gpu_info.h"
-// Fallbacks for CPU mode
-
-#ifdef _WIN32
-#include <sysinfoapi.h>
-void cpu_check_ram(mem_info_t *resp) {
-  resp->err = NULL;
-  MEMORYSTATUSEX info;
-  info.dwLength = sizeof(info);
-  if (GlobalMemoryStatusEx(&info) != 0) {
-    resp->total = info.ullTotalPhys;
-    resp->free = info.ullAvailPhys;
-    snprintf(&resp->gpu_id[0], GPU_ID_LEN, "0");
-  } else {
-    resp->err = LOAD_ERR();
-  }
-  return;
-}
-
-#elif __linux__
-#include <errno.h>
-#include <string.h>
-#include <sys/sysinfo.h>
-void cpu_check_ram(mem_info_t *resp) {
-  struct sysinfo info;
-  resp->err = NULL;
-  if (sysinfo(&info) != 0) {
-    resp->err = strdup(strerror(errno));
-  } else {
-    resp->total = info.totalram * info.mem_unit;
-    resp->free = info.freeram * info.mem_unit;
-    snprintf(&resp->gpu_id[0], GPU_ID_LEN, "0");
-  }
-  return;
-}
-
-#elif __APPLE__
-// TODO consider an Apple implementation that does something useful
-// mem_info_t cpu_check_ram() {
-//   mem_info_t resp = {0, 0, NULL};
-//   return resp;
-// }
-#else
-#error "Unsupported platform"
-#endif

gpu/gpu_info_cudart.c

@@ -40,7 +40,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
 
   for (i = 0; l[i].s != NULL; i++) {
     *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
-    if (!l[i].p) {
+    if (!*(l[i].p)) {
       char *msg = LOAD_ERR();
       LOG(resp->ch.verbose, "dlerr: %s\n", msg);
       UNLOAD_LIBRARY(resp->ch.handle);
@@ -94,7 +94,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
 }
 
 
-void cudart_check_vram(cudart_handle_t h, int i, mem_info_t *resp) {
+void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
   resp->err = NULL;
   cudartMemory_t memInfo = {0,0,0};
   cudartReturn_t ret;
@@ -166,9 +166,11 @@ void cudart_check_vram(cudart_handle_t h, int i, mem_info_t *resp) {
 
   resp->total = memInfo.total;
   resp->free = memInfo.free;
+  resp->used = memInfo.used;
 
   LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
   LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
+  LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 }